Systems and methods for automated voxelation of regions of interest for magnetic resonance spectroscopy

ABSTRACT

A system and method for automating an appropriate voxel prescription in a uniquely definable region of interest (ROI) in a tissue of a patient is provided, such as for purpose of conducting magnetic resonance spectroscopy (MRS) in the ROI. The dimensions and coordinates of a single three dimensional rectilinear volume (voxel) within a single region of interest (ROI) are automatically identified. This is done, in some embodiments by: (1) applying statistically identified ROI search areas within a field of view (FOV); (2) image processing an MRI image to smooth the background and enhance a particular structure useful to define the ROI; (3) identifying a population of pixels that define the particular structure; (4) performing a statistical analysis of the pixel population to fit a 2D model such as an ellipsoid to the population and subsequently fit a rectilinear shape within the model; (5) repetiting elements (1) through (4) using multiple images that encompass the 3D ROI to create a 3D rectilinear shape; (6) a repetition of elements (1) through (5) for multiple ROIs with a common FOV. A manual interface may also be provided, allowing for override to replace by manual prescription, assistance to identify structures (e.g. clicking on disc levels), or modifying the automated voxel (e.g. modify location, shape, or one or more dimensions).

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/061,798, filed Mar. 4, 2016, and titled “SYSTEMS AND METHODS FORAUTOMATED VOXELATION OF REGIONS OF INTEREST FOR MAGNETIC RESONANCESPECTROSCOPY,” which is a continuation of U.S. patent application Ser.No. 13/830,632, filed Mar. 14, 2013, and titled “SYSTEMS AND METHODS FORAUTOMATED VOXELATION OF REGIONS OF INTEREST FOR MAGNETIC RESONANCESPECTROSCOPY,” which is a continuation-in-part of PCT InternationalPatent Application Number PCT/US2011/062137 (Publication Number WO2012/071566), filed Nov. 23, 2011, and titled “SYSTEMS AND METHODS FORAUTOMATED VOXELATION OF REGIONS OF INTEREST FOR MAGNETIC RESONANCESPECTROSCOPY,” which claims the benefit of U.S. Provisional PatentApplication No. 61/417,182, filed Nov. 24, 2010, and titled “SYSTEMS ANDMETHODS FOR AUTOMATED VOXELATION OF REGIONS OF INTEREST FOR SINGLE VOXELMAGNETIC RESONANCE SPECTROSCOPY.” The entirety of each of these relatedpriority patent applications is hereby incorporated by reference andmade a part of this specification for all that it discloses.

INCORPORATION BY REFERENCE

The following disclosures are hereby incorporated by reference in theirentirety and made a part of this specification for all that theydisclose: U.S. Patent Publication No. 2008/0039710, filed Jul. 27, 2007,and titled “SYSTEM AND METHODS USING NUCLEAR MAGNETIC RESONANCE (NMR)SPECTROSCOPY TO EVALUATE PAIN AND DEGENERATIVE PROPERTIES OF TISSUE”;U.S. Patent Publication No. 2009/0030308, filed Mar. 21, 2008, andtitled “SYSTEM, COMPOSITION, AND METHODS FOR LOCAL IMAGING AND TREATMENTOF PAIN”; International Patent Publication No. WO 2009/148550, filed May29, 2009, and titled “BIOMARKERS FOR PAINFUL INTERVERTEBRAL DISCS ANDMETHODS OF USE THEREOF”; U.S. Patent Publication No. 2011/0087087, filedOct. 14, 2009, and titled “MR SPECTROSCOPY SYSTEM AND METHOD FORDIAGNOSING PAINFUL AND NON-PAINFUL INTERVERTEBRAL DISCS”; andInternational Patent Publication No. WO 2011/047197, filed Oct. 14,2010, and titled “MR SPECTROSCOPY SYSTEM AND METHOD FOR DIAGNOSINGPAINFUL AND NON-PAINFUL INTERVERTEBRAL DISCS”.

BACKGROUND Field of the Disclosure

The present disclosure relates to segmentation of electronic images tospecific regions of interest in a field of view, such as for exampletissue structures in medical imaging; and also for improvements toclinical magnetic resonance spectroscopy (MRS), for example, to singleor multi-voxel MRS; and also to the automated prescription of voxelswithin regions of interest where MRS is to be performed, such as forexample in intervertebral discs.

Description of the Related Art

Conventional systems and methods for prescribing voxel size and positionwithin a region of interest (ROI) for magnetic resonance spectroscopy(MRS) applications involve manual techniques by an operator technicianworking from magnetic resonance imaging (MRI) images taken prior to theprescription operation. This standard approach suffers from variousdrawbacks. These include, for example but without limitation: (1)limitations in the ability to accurately define the ROI boundarieswithin which the voxel should be fit; and (2) drawbacks from manualprescription such as (a) time required to perform the prescription undertime constraints of an overall exam, and (b) achieving optimal trade offbetween (i) maximizing voxel volume within the ROI for maximumsignal:noise ratio (SNR) and (ii) confining the voxel within only theROI boundaries and avoiding overlap with adjacent tissues outside of theROI—which can potentially blend chemical information between tissueconstituents within the ROI and extraneous constituents outside of theROI into the acquired spectra, and thus potentially compromisediagnostic interpretation and results.

In addition, diagnostic imaging exams are typically reliant upon a fixedpositioning of the patient during the exam, and may be compromised bypatient motion during the image acquisition. This also directly applies,for example but without limitation, to MRS exams. In particular, anaccurate voxel prescription which aligns the region for MRS dataacquisition to an ROI will become mis-registered with that tissue ROI ifthe patient moves after the prescription but before completion of theimage acquisition. The information acquired may blur pre- andpost-motion information during the image acquisition process, and/or mayintroduce chemical information from tissues which originally wereextraneous to the voxel location, but due to the motion were introducedinto the voxel location due to moving the anatomy relative to the fixedvoxel.

These issues represent particularly poignant challenges for conductingMRS in musculoskeletal applications, in particular skeletal joints, andstill more particularly intervertebral discs. This is also especiallyfor example the case in settings where, but without limitation, targettissue ROIs have limited volumes, requiring maximum voxel volume toachieve sufficient SNR, and are located adjacent to other tissues (e.g.next to or between bones, such as for example in skeletal joints) withdramatically different chemical constituents than the ROI—and thus couldintroduce significant unintended chemical signatures into acquiredspectra if there is voxel overlap outside the ROI or due to patientmotion during an exam.

In the particularly unique setting of intervertebral discs, the disctissues are bordered by opposite end-plates of superior and inferiorvertebral bodies, in addition to laterally by a number of differenttissue structures (e.g. spinal canal). These introduce dramaticallyhigher contents of lipid (in the case of bony structures), and water(e.g. in the case of spinal canal), than in the disc itself. Moreover,the discs are relatively small for conventional MRS voxel purposes. Thisis further confounded by prevalent disease conditions where diagnosticimaging (and MRS in particular) may often be indicated, such asdegenerative disc disease, that are specifically characterized byabnormally reduced disc height and volume as well as dehydration anddessication of the disc tissue. These issues represent a landscape thatis more challenging for defining (e.g. “segmenting”) the disc materialROI from surrounding structures, such as for example for diagnosticimage analysis or to define regions for directed therapies. Inparticular context of MRS, they also represent an environment forinherently low SNR, and accordingly require maximum possible voxelvolume to be prescribed. Furthermore, the relatively small geographiesand close proximities of discs relative to their bordering tissuesheightens the risks and potential impact of patient motion during adisc-related imaging exam, such as especially but not limited to discMRS exams.

These issues noted above are uniquely implicated in the ability tosuccessfully perform single voxel spectroscopy in skeletal joints, andespecially intervertebral discs, though they also relate to multi-voxelspectroscopy, and other imaging considerations (MR-related orotherwise), and other tissue structures.

SUMMARY OF SOME EMBODIMENTS

The current disclosure includes, among other aspects, certain solutionswhich address and overcome one or more of issues noted above. While suchsolutions are herein presented as uniquely tailored and beneficial foraddressing the specific challenges relative to particular anatomies andrelated considerations, they will also be applicable and presentbenefits elsewhere in other anatomies and/or indications or purposes.

Accordingly, certain aspects of this disclosure provide, and address andovercome a need for, a reliable automated system and method forsegmenting target tissue regions of interest (ROIs) from medicaldiagnostic images.

Other aspects of this disclosure provide, and address and overcome aneed for, a reliable automated system and method for prescribing a voxelin an ROI for magnetic resonance spectroscopy (MRS) applications.

Still other aspects of this disclosure provide, and address and overcomea need for, a reliable automated system and method for identifyingpatient motion during an imaging exam.

Specific modes of these aspects are in particular tailored and suitablefor providing beneficial use in, without limitation: skeletal joints,and in particular connective tissue regions between bones of suchjoints, and in particular intervertebral discs.

The disclosed systems and methods also provide useful, beneficialsolutions for other applications, including for example but not limitedto image data post-processing and analysis (e.g. quantification),directed therapies targeting tissue ROIs defined by such segmentation,patient motion assessment during an imaging exam (and potentiallyincluding dynamic adjustment of the imaging parameters), and single andmulti-voxel MRS (including without limitation automated voxelprescription).

Various embodiments disclosed in the present disclosure relate tosystems and methods for locating, analyzing, or otherwise obtaininginformation relating to a region of interest related to an electronicimage (and real world spatial coordinates represented by such images).Various embodiments relate to automated voxelation of regions ofinterest for single (or multi-) voxel magnetic resonance spectroscopy.Various features are described below and can be used in variouscombinations with each other. Many combinations of the featuresdescribed below will be apparent which are not specifically discussedand are a part of this disclosure, as would be apparent to one ofordinary skill.

One aspect of the present disclosure includes one or more computerreadable media comprising computer instructions configured to cause oneor more computer processors to perform actions comprising:

-   -   accessing an electronic image of an area that includes a region        of interest;    -   processing the electronic image to emphasize pixels associated        with at least one structure useful for identifying the region of        interest;    -   identifying a population of pixels in the electronic image        associated with the at least one structure;    -   selecting one or more image coordinates based on the population        of pixels;

and

-   -   converting the image coordinates to world coordinates        corresponding to at least a portion of the region of interest.        This aspect of the disclosure can be combined with the other        aspects, modes, embodiments, variations, and features described        herein to form various combinations and sub-combinations.

According to one mode of the present disclosure, the electronic imagecomprises a magnetic resonance imaging (MRI) image.

According to one mode of the present disclosure, selecting imagecoordinates comprises selecting a two dimensional shape that covers aselected area of the region of interest when converted to worldcoordinates.

According to one embodiment of the present disclosure, the twodimensional shape is rectilinear.

According to one mode of the present disclosure, selecting imagecoordinates comprises calculating a two dimensional model thatapproximates the region of interest based on the population of pixels.

According to one embodiment of the present disclosure, calculating thetwo dimensional model comprises applying an expectation maximizationalgorithm for estimating parameters of one or more Gaussiandistributions for the population of pixels.

According to one embodiment of the present disclosure, the computerinstructions are configured to cause the one or more computer processorsto position a two dimensional shape based on the two dimensional modelto cover a selected area of the region of interest.

According to one mode of the present disclosure, the computerinstructions are further configured to cause the one or more computerprocessors to output information relating to the region of interestbased on the world coordinates, the information comprising one or moreof a location, an orientation, a shape, an area, and a volume of theregion of interest.

According to one mode of the present disclosure, the computerinstructions are configured to cause the one or more computer processorsto perform additional actions comprising:

-   -   accessing one or more additional electronic images of one or        more areas that also include the region of interest;    -   processing the one or more additional electronic images to        emphasize pixels that are associated with the at least one        structure useful for identifying the region of interest in the        one or more additional electronic images;    -   identifying one or more additional populations of pixels in the        corresponding one or more additional electronic images, the one        or more additional populations of pixels being associated with        the at least one structure;    -   selecting one or more additional image coordinates from the one        or more additional electronic images, the one or more additional        image coordinates being based on the one or more additional        populations of pixels; and    -   converting the one or more additional image coordinates to world        coordinates corresponding to at least a portion of the region of        interest.

According to one embodiment of the present disclosure, the electronicimages are of slices substantially parallel to, and spaced apart from,each other.

According to one embodiment of the present disclosure, the worldcoordinates define a three dimensional selected volume of the region ofinterest.

According to one variation of the present disclosure, the computerreadable media is configured to be used with a magnetic resonancespectroscopy (MRS) system in communication with the one or more computerprocessors, wherein the MRS system is configured to provide an MRSspectrum of chemical constituents within the three dimensional selectedvolume.

According to one variation of the present disclosure, the computerinstructions are further configured to cause the one or more computerprocessors to analyze a post-acquisition scan to determine whether theregion of interest moved during an MRS acquisition.

According to one variation of the present disclosure, the region ofinterest is a nucleus of an intervertebral disc of a spine.

According to one variation of the present disclosure, the computerinstructions are configured to analyze the MRS spectrum and to determinewhether the three dimensional selected volume was likely mis-prescribedbased on one or more signals.

According to one variation of the present disclosure, the one or moresignals comprise a lipid signal.

According to one variation of the present disclosure, the computerinstructions are configured to provide a single three dimensional scanvolume to the MRS system configured for single voxel MRS.

According to one variation of the present disclosure, the computerinstructions are configured to cause the one or more computer processorsto select multiple three dimensional volumes corresponding to multipleregions of interest and to provide the multiple three dimensionalvolumes to the MRS system configured for multivoxel MRS.

According to one variation of the present disclosure, the electronicimage is of a first acquisition mode, and the MRS spectrum is of asecond acquisition mode different than the first acquisition mode.

According to one embodiment of the present disclosure, the threedimensional selected volume is a rectilinear volume.

According to one embodiment of the present disclosure, the computerinstructions are configured to cause the one or more computer processorsto define a two dimensional shape using the initial electronic image,the two dimensional shape corresponding to a cross sectional shape ofthe three dimensional selected volume, and to modify the cross sectionalshape of the three dimensional selected volume to fit the region ofinterest corresponding to at least one of the one or more additionalelectronic images.

According to one embodiment of the present disclosure, the computerinstructions are configured to cause the one or more computer processorsto define a plurality of two dimensional shapes associated with theregion of interest for the corresponding electronic images, and whereinthe three dimensional selected volume has a cross sectional shapecorresponding to the overlapping area of the plurality of twodimensional shapes.

According to one embodiment of the present disclosure, the computerreadable media can be configured to be used with a patient therapysystem configured to provide a therapy procedure to a patient based atleast in part on the three dimensional selected volume.

According to one variation of the present disclosure, the patienttherapy system is a radiation therapy system or an ultrasound therapysystem configured to direct energy to the three dimensional selectedvolume.

According to one embodiment of the present disclosure, the computerinstructions are configured to cause the one or more computer processorsto calculate a three dimensional model that approximates the region ofinterest based on the populations of pixels from the electronic images.

According to one embodiment of the present disclosure, the electronicimages are magnetic resonance imaging (MRI) images and the computerinstructions are configured to cause the one or more computer processorsto receive the MRI images from an MRI system in communication with theone or more computer processors.

According to one mode of the present disclosure, processing theelectronic image comprises smoothing the electronic image.

According to one embodiment of the present disclosure, smoothing theelectronic image comprises modifying a brightness value for a pixelbased on the brightness of neighboring pixels.

According to one variation of the present disclosure, the neighboringpixels comprise one or more pixels from one or more additionalneighboring electronic images.

According to one mode of the present disclosure, processing theelectronic image comprises performing at least one top-hat filteringoperation.

According to one mode of the present disclosure, processing theelectronic image comprises performing at least one morphological imageprocessing operation.

According to one embodiment of the present disclosure, processing theelectronic image comprises performing a first top-hat filteringoperation on an upper portion of the spine and performing a secondtop-hat filtering operation on a lower curved portion of the spine.

According to one mode of the present disclosure, processing theelectronic image comprises performing an order statistic filteringoperation.

According to one mode of the present disclosure, the at least onestructure comprises the region of interest, the computer instructionsbeing configured to cause the one or more computer processors to processthe electronic image to emphasize the region of interest.

According to one mode of the present disclosure, the at least onestructure comprises at least one structure adjacent to the region ofinterest.

According to one mode of the present disclosure, indentifying thepopulation of pixels in the electronic image comprises analyzing pixelsin a predefined search area of the electronic image and assigninganalyzed pixels having a particular property to the population of pixelsassociated with the at least one structure.

According to one embodiment of the present disclosure, the particularproperty is a pixel brightness intensity.

According to one embodiment of the present disclosure, the predefinedsearch area is based on statistical analysis of historical data relatingto a likely location for the at least one structure.

According to one mode of the present disclosure, indentifying thepopulation of pixels in the electronic image is based on input from anoperator, the input comprising one or more locations associated with theat least one structure.

According to one mode of the present disclosure, the computerinstructions are further configured to cause the one or more computerprocessors to analyze the population of pixels based on at least onereliability criteria.

According to one mode of the present disclosure, the at least onereliability criteria comprises a comparison of a number of pixels in thepopulation of pixels to a threshold pixel number.

According to one embodiment of the present disclosure, when thepopulation of pixels does not satisfy the reliability criteria, thecomputer instructions cause the one or more processors to flag thepopulation of pixels for review by a user.

According to one embodiment of the present disclosure, when thepopulation of pixels does not satisfy the reliability criteria, thecomputer instructions cause the one or more processors to employadditional algorithms to modify the population of pixels to improvereliability.

One aspect of the present disclosure is a system for obtaininginformation relating to a region of interest, the system comprising:

-   -   a one or more computer readable media according to (or        configured to perform the method of) any one of the aspects,        modes, embodiments, or variations identified herein; and    -   one or more computer processors in communication with the        computer readable media for executing the computer instructions.        This aspect of the disclosure can be combined with the other        aspects, modes, embodiments, variations, and features described        herein to form various combinations and sub-combinations.

One mode of the present disclosure further comprises a magneticresonance spectroscopy (MRS) system in communication with the one ormore computer processors, wherein the MRS system is configured toprovide an MRS spectrum of chemical constituents within the region ofinterest.

One mode of the present disclosure further comprises a magneticresonance imaging (MRI) system in communication with the one or morecomputer processors, wherein the MRI system is configured to provide aplurality of MRI images.

According to one mode of the present disclosure, the electronic image isof a first imaging mode, wherein the computer instructions are furtherconfigured to cause the one or more processors to access an additionalelectronic image of a second imaging mode different than the firstimaging mode, the additional electronic image being of the substantiallythe same area as the initial electronic image, wherein the worldcoordinates are based in part on the additional electronic image of thesecond imaging mode.

According to one embodiment of the present disclosure, the computerinstructions are further configured to cause the one or more processorsto:

-   -   process the additional electronic image to emphasize pixels in        the additional electronic image that are associated with the at        least one structure useful for identifying the region of        interest;    -   identify a population of pixels in the additional electronic        image associated with the at least one structure; and    -   combine information based on the population of pixels in the        additional electronic image with information based on the        population of pixels in the initial electronic image to generate        the world coordinates.

According to one mode of the present disclosure, the computerinstructions are further configured to cause the one or more processorsto:

-   -   process the additional electronic image to emphasize pixels in        the additional electronic image that are associated with the at        least one structure useful for identifying the region of        interest;    -   identify a population of pixels in the additional electronic        image associated with the at least one structure; and    -   compare information based on the population of pixels in the        additional electronic image with information based on the        population of pixels in the initial electronic image to generate        the world coordinates to evaluate accuracy of the world        coordinates.

One aspect of the present disclosure is a method for obtaininginformation relating to a region of interest, the method comprising:

-   -   accessing an electronic image of an area that includes a region        of interest;    -   processing the electronic image, using one or more computer        processors, to emphasize pixels associated with at least one        structure useful for identifying the region of interest;    -   identifying a population of pixels in the electronic image        associated with the at least one structure;    -   selecting one or more image coordinates based on the population        of pixels; and    -   converting the image coordinates to world coordinates        corresponding to at least a portion of the region of interest.        This aspect of the disclosure can be combined with the other        aspects, modes, embodiments, variations, and features described        herein to form various combinations and sub-combinations.

According to one mode of the present disclosure, the electronic imagecomprises a magnetic resonance imaging (MRI) image.

According to one embodiment of the present disclosure, selecting imagecoordinates comprises selecting a two dimensional shape that covers aselected area of the region of interest when converted to worldcoordinates.

According to one mode of the present disclosure, selecting imagecoordinates comprises calculating a two dimensional model thatapproximates the region of interest based on the population of pixels.

According to one embodiment of the present disclosure, calculating thetwo dimensional model comprises applying an expectation maximizationalgorithm for estimating parameters of one or more Gaussiandistributions for the population of pixels.

According to one embodiment of the present disclosure, the computerinstructions are configured to cause the one or more computer processorsto position a two dimensional shape based on the two dimensional modelto cover a selected area of the region of interest.

One mode of the present disclosure further comprises outputtinginformation relating to the region of interest based on the worldcoordinates, the information comprising one or more of a location, anorientation, a shape, an area, and a volume of the region of interest.

One mode of the present disclosure further comprises:

-   -   accessing one or more additional electronic images of one or        more areas that also include the region of interest;    -   processing the one or more additional electronic images, with        the one or more computer processors, to emphasize pixels that        are associated with the at least one structure useful for        identifying the region of interest in the one or more additional        electronic images;    -   identifying one or more additional populations of pixels in the        corresponding one or more additional electronic images, the one        or more additional populations of pixels being associated with        the at least one structure;    -   selecting one or more additional image coordinates from the one        or more additional electronic images, the one or more additional        image coordinates being based on the one or more additional        populations of pixels; and    -   converting the one or more additional image coordinates to world        coordinates corresponding to at least a portion of the region of        interest.

According to one embodiment of the present disclosure, the worldcoordinates define to a three dimensional selected volume of the regionof interest.

One variation of the present disclosure further comprises scanning thethree dimensional selected volume with a magnetic resonance spectroscopy(MRS) system in communication with the one or more computer processorsto provide an MRS spectrum of chemical constituents within the threedimensional selected volume.

One variation of the present disclosure further comprises analyzing apost-acquisition scan, using the one or more computer processors, todetermine whether the region of interest moved during an MRSacquisition.

According to one variation of the present disclosure, the region ofinterest is a nucleus of an intervertebral disc of a spine.

One variation of the present disclosure further comprises analyzing theMRS spectrum, using the one or more computer processors, to determinewhether the three dimensional selected volume was likely mis-prescribedbased on one or more signals.

One variation of the present disclosure further comprises defining oneor more additional three dimensional selected volumes covering at leastportions of one or more additional regions of interest, and scanning theadditional three dimensional selected volumes one at a time with the MRSsystem using single voxel MRS.

One variation of the present disclosure further comprises defining oneor more additional three dimensional selected volumes covering at leastportions of one or more additional regions of interest, and scanning theadditional three dimensional selected volumes simultaneously with theMRS system using multivoxel MRS.

According to one variation of the present disclosure, the electronicimage is of a first acquisition mode, and wherein the MRS spectrum is ofa second acquisition mode different than the first acquisition mode.

One variation of the present disclosure further comprises:

-   -   defining a two dimensional shape using the initial electronic        image, the two dimensional shape corresponding to a cross        sectional shape of the three dimensional selected volume; and    -   modifying the cross sectional shape of the three dimensional        selected volume to fit the region of interest corresponding to        at least one of the one or more additional electronic images.

One variation of the present disclosure further comprises defining aplurality of two dimensional shapes associated with the region ofinterest for the corresponding electronic images, wherein the threedimensional selected volume has a cross sectional shape corresponding tothe overlapping area of the plurality of two dimensional shapes.

One variation of the present disclosure further comprises using with apatient therapy system to provide a therapy procedure to a patient basedat least in part on the three dimensional selected volume.

According to one variation of the present disclosure, the patienttherapy system is a radiation therapy system or an ultrasound therapysystem configured to direct energy to the three dimensional selectedvolume.

One embodiment of the present disclosure further comprises calculating athree dimensional model that approximates the region of interest basedon the populations of pixels from the electronic images.

According to one embodiment of the present disclosure, the electronicimages comprise magnetic resonance imaging (MRI) images, and the methodfurther comprising acquiring the MRI images using an MRI system incommunication with the one or more computer processors.

According to one mode of the present disclosure, processing theelectronic image comprises smoothing the electronic image.

According to one embodiment of the present disclosure, smoothing theelectronic image comprises modifying a brightness value for a pixelbased on the brightness of neighboring pixels.

According to one embodiment of the present disclosure, the neighboringpixels comprise one or more pixels from one or more additionalneighboring electronic images.

According to one mode of the present disclosure, processing theelectronic image comprises performing at least one top-hat filteringoperation.

According to one mode of the present disclosure, processing theelectronic image comprises performing at least one morphological imageprocessing operation.

According to one embodiment of the present disclosure, processing theelectronic image comprises performing a first top-hat filteringoperation on an upper portion of the spine and performing a secondtop-hat filtering operation on a lower curved portion of the spine.

According to one mode of the present disclosure, processing theelectronic image comprises performing an order statistic filteringoperation.

According to one mode of the present disclosure, the at least onestructure comprises the region of interest, wherein processing theelectronic image comprises emphasizing the region of interest.

According to one mode of the present disclosure, the at least onestructure comprises at least one structure adjacent to the region ofinterest.

According to one mode of the present disclosure, indentifying thepopulation of pixels in the electronic image comprises analyzing pixelsin a predefined search area of the electronic image and assigninganalyzed pixels having a particular property to the population of pixelsassociated with the at least one structure.

According to one embodiment of the present disclosure, the particularproperty is a pixel brightness intensity.

According to one embodiment of the present disclosure, the predefinedsearch area is based on statistical analysis of historical data relatingto a likely location for the at least one structure.

One mode of the present disclosure further comprises receiving inputfrom an operator, the input comprising one or more locations associatedwith the at least one structure, and wherein identifying the populationof pixels in the electronic image is based on the input.

One mode of the present disclosure further comprises analyzing thepopulation of pixels, using the one or more computer processors, basedon at least one reliability criteria.

According to one embodiment of the present disclosure, the at least onereliability criteria comprises a comparison of a number of pixels in thepopulation of pixels to a threshold pixel number.

One embodiment of the present disclosure further comprises, when thepopulation of pixels does not satisfy the reliability criteria, flaggingthe population of pixels for review by a user.

One embodiment of the present disclosure further comprises, when thepopulation of pixels does not satisfy the reliability criteria,employing additional algorithms to modify the population of pixels toimprove reliability.

According to one mode of the present disclosure, each of the actionsrecited is performed by the one or more processors.

According to one mode of the present disclosure, the electronic image isof a first imaging mode, and wherein the method further comprisesaccessing an additional electronic image of a second imaging modedifferent than the first imaging mode, the additional electronic imagebeing of the substantially the same area as the initial electronicimage, wherein the world coordinates are based in part on the additionalelectronic image of the second imaging mode.

One mode of the present disclosure further comprises:

-   -   processing the additional electronic image to emphasize pixels        in the additional electronic image that are associated with the        at least one structure useful for identifying the region of        interest;    -   identify a population of pixels in the additional electronic        image associated with the at least one structure; and    -   combining information based on the population of pixels in the        additional electronic image with information based on the        population of pixels in the initial electronic image to generate        the world coordinates.

One embodiment of the present disclosure further comprises:

-   -   processing the additional electronic image to emphasize pixels        in the additional electronic image that are associated with the        at least one structure useful for identifying the region of        interest;    -   identify a population of pixels in the additional electronic        image associated with the at least one structure; and    -   comparing information based on the population of pixels in the        additional electronic image with information based on the        population of pixels in the initial electronic image to generate        the world coordinates to evaluate accuracy of the world        coordinates.

One aspect of the present disclosure is a method for prescribing a shapewithin a region of interest (ROI) in an electronic image of a bodyportion of a patient, comprising:

-   -   defining the ROI in the electronic image;    -   prescribing the shape to fit within the ROI in the electronic        image; and    -   using one or more processors to process the electronic image to        perform at least one of defining the ROI and prescribing the        shape. This aspect of the disclosure can be combined with the        other aspects, modes, embodiments, variations, and features        described herein to form various combinations and        sub-combinations.

One aspect of the present disclosure is a method for configuring amedical system to be used in performing an operation on a region ofinterest (ROI) in a body portion of a patient, the method comprising:

-   -   using one or more processors to process an electronic image to        define the ROI in the electronic image; and    -   configuring the medical system in a configuration that is        operable to perform the operation on at least a portion of the        ROI. This aspect of the disclosure can be combined with the        other aspects, modes, embodiments, variations, and features        described herein to form various combinations and        sub-combinations.

One aspect of the present disclosure is a method for defining a regionof interest (ROI) between bones in an electronic image of a body portioncomprising a skeletal joint in a patient, the method comprising:

-   -   using one or more processors to process the electronic image to        identify a region between the bones and bordered at least in        part by the bones, and to define the ROI to coincide with at        least a part of the region. This aspect of the disclosure can be        combined with the other aspects, modes, embodiments, variations,        and features described herein to form various combinations and        sub-combinations.

One mode of the present disclosure further comprises configuring amedical system in a configuration that is operable to perform anoperation on the ROI.

According to one mode of the present disclosure, the body portioncomprises a skeletal joint and the ROI is located at least in partbetween bones of the skeletal joint, the method further comprising usingthe one or more processors to process the electronic image to identify aregion between the bones and bordered at least in part by the bones, andto define the ROI to coincide with at least a part of the region.

One embodiment of the present disclosure further comprises configuring amedical system in a configuration that is operable to perform anoperation on the ROI.

According to one mode of the present disclosure, the body portioncomprises a skeletal joint and the ROI is located at least in partbetween bones of the skeletal joint, and further comprising using theone or more processors to process the electronic image to identify aregion between the bones and bordered at least in part by the bones, andto define the ROI to coincide with at least a part of the region.

One mode of the present disclosure further comprises using the one ormore processors to process the electronic image to define the ROI.

One mode of the present disclosure further comprises using the one ormore processors to process the electronic image to prescribe the shape.

According to one embodiment of the present disclosure, prescribing theshape is entirely performed using the one or more processors.

According to one embodiment of the present disclosure, prescribing theshape is partially performed using the one or more processors.

One mode of the present disclosure further comprises prescribing a shapeto fit within the ROI.

One embodiment of the present disclosure further comprises using the oneor more processors to process the electronic image to prescribe theshape.

One mode of the present disclosure further comprises prescribing a shapeto fit within the ROI.

One embodiment of the present disclosure further comprises using the oneor more processors to process the electronic image to prescribe theshape.

According to one mode of the present disclosure, said electronic imagecomprises a 2D planar image.

One embodiment of the present disclosure further comprises using the oneor more processors to process the 2D planar image to define the ROI as a2D ROI in the 2D planar image.

One variation of the present disclosure further comprises using the oneor more processors to process the 2D planar image to prescribe a 2Dshape to fit within the 2D ROI.

One embodiment of the present disclosure further comprises using the oneor more processors to process the 2D planar image to prescribe a 2Dshape to fit within a 2D ROI.

According to one mode of the present disclosure, said electronic imagecomprises a 3D electronic image constructed from a series of spatiallyunique but related 2D planar images of the body portion, and furthercomprising using the one or more processors to: process multiple said 2Dplanar images within the series to define multiple respective 2D ROIstherein, and to construct a 3D ROI in the 3D image from the multiple 2DROIs.

According to one embodiment of the present disclosure, the 3D ROIcorresponds with a definable structure within the body portion, andfurther comprising using the one or more processors to process the 3Delectronic image to define the 3D ROI by applying a template map to eachof said plurality of 2D planar images and providing a default regionpredictive of locating said structure in each said respective 2D planarimages based on prior knowledge derived from other similar 2D planarimages from other patients, and processing the default region in the 2Dplanar image to define the 2D ROI from which the 3D ROI is constructed.

One variation of the present disclosure further comprises using the oneor more processors to process multiple of the 2D planar images toprescribe a 3D shape to fit within the 3D ROI in the 3D image.

According to one mode of the present disclosure, the ROI correspondswith a definable structure within the body portion, and furthercomprising using the one or more processors to process the electronicimage to define the ROI using a template map providing a default regionpredictive of locating said structure based on location informationderived from other electronic images from other patients, and processingthe default region in the electronic image to define the ROI.

According to one embodiment of the present disclosure, the defaultregion processing comprises at least one of edge detection and acontrast filter.

One mode of the present disclosure further comprises using the one ormore processors to process the electronic image to define the ROI usingedge detection.

One mode of the present disclosure further comprises using the one ormore processors to process the electronic image to define the ROI usinga contrast filter.

One embodiment of the present disclosure further comprises using the oneor more processors to process the electronic image to define the ROIusing edge detection.

One variation of the present disclosure further comprises prescribingthe 3D shape to achieve a criteria related to volume or dimension of the3D shape.

According to one variation of the present disclosure, the criteriacomprises a maximum contained volume or dimension within the 3D ROI.

One variation of the present disclosure further comprises:

-   -   using the one or more processors to process the electronic image        to determine an initial 3D ROI and to apply an inward        dimensional off-set from an outer boundary of the initial 3D ROI        to define an off-set 3D region in the image; and    -   using the one or more processors to define the ROI to coincide        at least in part with the off-set 3D region.

One variation of the present disclosure further comprises using the oneor more processors to process the electronic image to prescribe multiplesaid 3D shapes within the 3D ROI.

One variation of the present disclosure further comprises prescribingthe multiple 3D shapes to collectively achieve a criteria relating tovolume of the 3D shapes.

According to one variation of the present disclosure, the criteriacomprises a maximum contained volume within the 3D ROI.

One variation of the present disclosure further comprises using the oneor more processors to apply an inward dimensional off-set from an outerboundary of the 3D ROI for form an off-set 3D ROI, and prescribing themultiple 3D shapes to collectively achieve a criteria relating to avolume of the 3D shapes.

According to one variation of the present disclosure, the criteriacomprises a maximum contained volume within the off-set 3D ROI.

One variation of the present disclosure further comprises configuring amedical system to perform an operation on at least a part of the 3D ROIcorresponding with the multiple 3D shapes.

According to one variation of the present disclosure, the medical systemcomprises a nuclear magnetic resonance (MR) system, and furthercomprising:

-   -   configuring the MR system in a configuration that is operable in        an operating mode to acquire MR-based data from at least the        part of the 3D ROI, such that multiple portions of the MR-based        data acquired in the operating mode correspond with each of the        multiple 3D shapes; and    -   correlating the multiple portions of the MR-based data with        unique locations of the respective multiple 3D shapes.

According to one variation of the present disclosure, the configurationcomprises a T2-weighted imaging sequence.

According to one variation of the present disclosure, the configurationcomprises a T1-weighted imaging sequence.

According to one variation of the present disclosure, the configurationcomprises an MR spectroscopy (MRS) pulse sequence.

According to one variation of the present disclosure, the configurationcomprises a T1-rho pulse sequence.

One variation of the present disclosure further comprises correlating avalue of the MR-based data for each of the 3D shapes with a diagnosticcriteria.

One variation of the present disclosure further comprises displaying anindicia related to the correlation for each of the 3D shapes as anoverlay to the electronic image.

One variation of the present disclosure further comprises defining the3D ROI and prescribing the 3D shapes before configuring the operatingthe medical system in the configuration.

One variation of the present disclosure further comprises using thedefined 3D ROI and 3D shape prescriptions to configure the medicalsystem in the configuration.

One variation of the present disclosure further comprises defining the3D ROI and prescribing the 3D shapes after configuring the medicalsystem in the configuration and after operating the medical system inthe operating mode.

One variation of the present disclosure further comprises using the 3Dshapes to correlate the operation corresponding with portions of the ROIrepresented by the respective 3D shapes.

One variation of the present disclosure further comprises:

using the one or more processors to prescribe said multiple 3D shapes asvoxels; and

after prescribing the voxels, configuring a magnetic resonancespectroscopy (MRS) system in a configuration that is operable to acquireMRS information from each of the voxels.

One mode of the present disclosure further comprises using the one ormore processors to process the electronic image to prescribe a singleshape that is a different shape than the ROI to fit within the ROI toachieve a criteria relating to volume or dimension of the single shape.

According to one embodiment of the present disclosure, the criteriacomprises a maximized dimension or contained volume within the singleshape.

One mode of the present disclosure further comprises:

-   -   using the one or more processors to process the electronic image        to determine an initial ROI comprising an outer boundary, and to        apply an inward dimensional off-set from the outer boundary to        define an off-set region in the image; and    -   using the one or more processors to define the ROI to coincide        at least in part with the off-set region.

According to one mode of the present disclosure, the ROI and the shapecomprise different respective geometries.

According to one embodiment of the present disclosure, the ROI comprisesa geometry comprising at least one non-straight linear edge boundary.

According to one variation of the present disclosure, the ROI comprisesat least in part a curvilinear edge boundary.

According to one embodiment of the present disclosure, the shapecomprises a rectilinear geometry.

One mode of the present disclosure further comprises prescribing theshape for achieving a criteria related to volume or dimension of theshape.

According to one embodiment of the present disclosure, the criteriacomprises a maximized dimension or contained volume within the ROI.

According to one mode of the present disclosure, the ROI comprises atleast a portion of an intervertebral disc between two superior andinferior respective vertebral bodies bordering the disc.

One embodiment of the present disclosure further comprises using the oneor more processors to define the ROI at least in part by locating twosuperior and inferior borders between the disc and the vertebral bodiesin the electronic image.

According to one variation of the present disclosure, the borderscomprise vertebral body end-plates.

One variation of the present disclosure further comprises defining theROI by defining at least one annular wall of the disc and connecting thevertebral bodies in the electronic image, such that the ROI is definedas a region contained between the borders and the at least one annularwall of the disc.

According to one variation of the present disclosure, the electronicimage comprises a 2D planar image through the disc and vertebral bodies,and further comprising defining the ROI by defining first and secondopposite portions of the annular wall in the electronic image, such thatthe ROI is defined as a region contained between the borders and the twoopposite portions of the annular wall.

According to one variation of the present disclosure, the electronicimage comprises a 2D planar image and each of the borders comprises aline first end and a second end, and further comprising defining the ROIby defining two connecting lines between first ends and second ends ofthe respective borders, thereby confining the ROI as an area containedwithin the borders and connecting lines.

According to one variation of the present disclosure, the ROI comprisesa nucleus portion of the intervertebral disc.

According to one variation of the present disclosure, the ROI comprisesan annulus portion of the intervertebral disc.

One variation of the present disclosure further comprises defining firstand second said ROIs comprising a nucleus portion and an annulus portionof the disc, respectively, and prescribing a first shape to fit withinthe first ROI and a second shape to fit within the second ROI.

One mode of the present disclosure further comprises using the one ormore processors to operate a computer program in a computer readablemedium for performing the processing of the electronic image.

One mode of the present disclosure further comprises configuring an MRsystem in a configuration that is operable to perform an MR operation onat least a portion of the ROI corresponding with the shape.

One embodiment of the present disclosure further comprises configuringthe MR system in a configuration that is operable to acquire aT1-weighted image of at least the portion of the ROI.

One embodiment of the present disclosure further comprises configuringthe MR system in a configuration that is operable to acquire aT2-weighted image of at least the portion of the ROI.

One embodiment of the present disclosure further comprises configuringthe MR system in a configuration that is operable to acquire T1-rho dataof at least the portion of the ROI.

One embodiment of the present disclosure further comprises configuringthe MR system in a configuration that is operable to acquire an MRspectroscopy data from at least the portion of the ROI.

One mode of the present disclosure further comprises configuring a CTsystem in a configuration that is operable to perform a CT imagingoperation on at least a portion of the ROI corresponding with the shape.

One mode of the present disclosure further comprises configuring anX-ray system in a configuration that is operable to perform an X-rayimaging operation on at least a portion of the ROI corresponding withthe shape.

One mode of the present disclosure further comprises configuring anuclear imaging system in a configuration that is operable to perform anuclear imaging operation on at least a portion of the ROI correspondingwith the shape.

One mode of the present disclosure further comprises configuring a PETimaging system in a configuration that is operable to perform a PETimaging operation on at least a portion of the ROI corresponding withthe shape.

One mode of the present disclosure further comprises configuring amedical diagnostic system in a configuration that is operable to performa medical diagnostic imaging operation on at least a portion of the ROIcorresponding with the shape.

According to one embodiment of the present disclosure, the medicaldiagnostic system comprises a combination of multiple unique imagingmodalities comprising first and second modalities, and furthercomprising configuring at least the first modality in a respective firstconfiguration to perform a first operation on at least the portion ofthe ROI.

One embodiment of the present disclosure further comprises configuringthe second modality in a respective second configuration to perform asecond operation on at least the portion of the ROI.

One variation of the present disclosure further comprises:

-   -   configuring the first modality in a configuration that is        operable to acquire the electronic image;    -   defining the ROI based upon the electronic image acquired by the        first modality; and    -   configuring the second modality in the second configuration        based upon the electronic image acquired by the first modality.

According to one variation of the present disclosure, the medicaldiagnostic system comprises a combination MR/CT, PET/CT, or PET/MRsystem.

According to one mode of the present disclosure, the body portioncomprises a foramen.

According to one mode of the present disclosure, the body portioncomprises a body space defined by at least one tissue wall.

According to one mode of the present disclosure, the body portioncomprises at least a portion of an organ.

According to one mode of the present disclosure, the body portioncomprises at least a portion of a prostate gland.

According to one mode of the present disclosure, the body portioncomprises at least a portion of a breast.

According to one mode of the present disclosure, the body portioncomprises at least a portion of a brain.

According to one mode of the present disclosure, the body portioncomprises at least a portion of a tumor.

According to one mode of the present disclosure, the body portioncomprises at least a portion of a bone.

According to one mode of the present disclosure, the electronic imagecomprises an image coordinate system, and further comprising definingthe ROI in image coordinates.

One embodiment of the present disclosure further comprises prescribingthe shape in image coordinates.

One variation of the present disclosure further comprises prescribingthe shape in world coordinates.

One mode of the present disclosure further comprises processing theelectronic image to define multiple ROIs in the electronic image.

According to one embodiment of the present disclosure, the multiple ROIscorrespond to multiple intervertebral discs.

One embodiment of the present disclosure further comprises prescribing aplurality of shapes to fit within the plurality of ROIs in theelectronic image.

One mode of the present disclosure further comprises:

-   -   using the one or more processors to process the electronic image        to determine a recommended shape to fit within the ROI; and    -   allowing a user to accept the recommended shape or to manually        prescribe a second shape as the shape.

According to one embodiment of the present disclosure, the allowingcomprises allowing the user to modify the recommended shape to prescribethe second shape as the shape.

According to one mode of the present disclosure, the electronic image isof a first imaging mode, the method further comprising modifying thedefined ROI or the prescribed shape based on an additional electronicimage of a second imaging mode different than the first imaging mode.

According to one mode of the present disclosure, the electronic image isof a first imaging mode, the method further comprising comparing thedefined ROI or the prescribed shape to information derived from anadditional electronic image of a second imaging mode different than thefirst imaging mode.

One aspect of the invention is a method for determining patient motionduring a medical procedure on a patient, comprising:

-   -   comparing a first image from the patient acquired at a first        time relative to the procedure against a second image acquired        from the patient at a second time relative to the procedure;    -   observing a difference between the first and second images based        upon the comparison; and    -   determining patient motion between the first and second times        based upon the difference between the first and second images.        This aspect of the disclosure can be combined with the other        aspects, modes, embodiments, variations, and features described        herein to form various combinations and sub-combinations.

One mode of the present disclosure further comprises mapping a voxelprescribed in a region of interest (ROI) based upon the first image ontosame coordinates in the second image.

One embodiment of the present disclosure further comprises determiningchange of voxel location relative to the ROI in the first and secondimages.

Another aspect of the present disclosure is a method for determiningmotion of a defined region of interest (ROI) of a body of a patient froma first position to a second position during a medical procedure beingconducted on a patient. This method according to one mode comprisescomparing a first image from the patient acquired at a first timerelative to the procedure against a second image acquired from thepatient at a second time relative to the procedure, wherein the firstand second images comprise the ROI; observing a difference between thefirst and second images based upon the comparison; and determiningmotion of the ROI between the first and second positions at the firstand second times, respectively, based upon the difference between thefirst and second images.

Another mode of this aspect comprises re-prescribing a voxel prescribedin a first prescription within the ROI based upon the first position inthe first image onto same voxel prescription coordinates in a secondprescription relative to the ROI in the second position in the secondimage.

Another mode comprises determining change of voxel location relative tothe ROI in the first and second images.

According to another mode, the determining comprises quantifying anextent of patient motion to a value.

One embodiment of this mode comprises comparing the value to a referencevalue to determine a difference value.

Another embodiment further comprises adjusting the procedure based uponthe difference value. According to one further embodiment, thisadjusting comprises terminating the procedure based upon the differencevalue. According to another embodiment, the adjusting comprises changinga spatial orientation or direction parameter of the procedure initiallyregistered with the ROI in the first position to register the procedurewith the ROI in the second position.

According to another embodiment, observing the difference is based uponadjusting one of the first and second images in overlay to the other ofthe first and second images while performing a correlation comparison ofan image parameter between the first and second images, determining theadjustment corresponding with maximum correlation of the imageparameter, and deriving the difference from the adjustment. In onefurther embodiment, the correlation comparison is based upon aregistration of a segmented structure between the first and secondinitial images. In another further embodiment, the segmented structurecomprises a border or shape of the ROI.

One aspect of the present disclosure is a medical device system,comprising one or more processors configured to run computer instructsstored on one or more computer readable media to perform one or more ofthe actions described herein. This aspect of the disclosure can becombined with the other aspects, modes, embodiments, variations, andfeatures described herein to form various combinations andsub-combinations.

One aspect of the present disclosure is one or more computer readablemedia comprising computer instructions configured to cause one or morecomputer processors to perform one or more of the actions describedherein. This aspect of the disclosure can be combined with the otheraspects, modes, embodiments, variations, and features described hereinto form various combinations and sub-combinations.

According to one mode of the present disclosure, the defining the ROI inthe electronic image comprises:

-   -   accessing the electronic image;    -   processing the electronic image, using one or more computer        processors, to emphasize pixels associated with at least one        structure useful for identifying the region of interest; and    -   identifying a population of pixels in the electronic image        associated with the at least one structure.

One embodiment of the present disclosure further comprises:

-   -   selecting one or more image coordinates based on the population        of pixels; and    -   converting the image coordinates to world coordinates        corresponding to at least a portion of the region of interest.

Another aspect of the present disclosure comprises one or morenon-transitory computer readable media comprising computer instructionsconfigured to cause one or more computer processors to perform thefollowing actions: performing image fusion between multiple initialelectronic images of a region of interest (ROI) of a body of a patientto generate a fused image providing an enhanced definition of the ROI orborder thereof relative to the initial images; and processing the fusedimage as the electronic image according to any one or more of the otheraspects, modes, embodiments, or variations elsewhere herein disclosed.In various embodiments, processing the fused image comprises obtaininginformation relating to the ROI, prescribing a shape within the ROI,configuring a medical system to be used in performing an operation onthe ROI, or defining the ROI between bones as described elsewhereherein.

According to one mode of this aspect, the initial images comprisemagnetic resonance images.

According to one embodiment of this mode, the initial images comprise aT1-weighted and a T2-weighted image.

In another mode, performing the image fusion comprises differencing theinitial images; and the fused image comprises a differenced image.

In another mode, performing the image fusion comprises blending theinitial images; and the fused image comprises a blended image. In oneembodiment of this mode, blending the initial images comprisesalpha-blending; and the blended image comprises an alpha-blended image.

In another mode, the non-transitory computer readable media comprisesfurther computer instructions configured to cause the one or moreprocessors to perform actions comprising enhancing contrast along apopulation of pixels corresponding with a border of the ROI using thefused image.

Another aspect of the current disclosure is a method for automateddiagnostic image processing, comprising performing image fusion betweenmultiple initial electronic images to generate a fused image;post-processing the fused image as the electronic image, such asaccording to any of the other aspects, modes, embodiments, or variationselsewhere herein disclosed; and causing one or more computer processorsto perform the image fusion and post-processing via a set of computerinstructions from one or more non-transitory computer readable media. Invarious embodiments, post-processing the fused image comprises obtaininginformation relating to the ROI, prescribing a shape within the ROI,configuring a medical system to be used in performing an operation onthe ROI, or defining the ROI between bones as described elsewhereherein.

In one mode of this aspect, the initial electronic images comprisemagnetic resonance images. According to one embodiment of this mode, theinitial electronic images comprise a T1-weighted and a T2-weightedimage.

In another mode, performing the image fusion comprises differencing theinitial images; and the fused image comprises a differenced image.

In another mode, performing the image fusion comprises blending theinitial images; and the fused image comprises a blended image. Accordingto one embodiment of this mode, blending the initial images comprisesalpha-blending; and the blended image comprises an alpha-blended image.

In another mode, the actions of the processor conducted according to thecomputer instructions further comprise enhancing contrast along apopulation of pixels corresponding with a border of the ROI via thefused image.

The aspects, modes, embodiments, variations, and features noted above,and those noted elsewhere herein, can be combined to form variouscombinations and sub-combinations, even where not specificallydiscussed. For example, the methods and systems disclosed herein canperform one or more of the operations shown in FIG. 5 or describedherein alone or with various combinations of the other operations shownin FIG. 5 or disclosed herein.

As would be apparent to one of ordinary skill, use of particular termsat specific places in this disclosure above shall be considered torelate consistently to similar features as other uses of same terms inother places in this disclosure, including in context of providingcertain combinations between then and which are contemplated hereunder(though such combinations are not necessarily required limitations);provided, however, to the extent such different uses are compatible andwould not create inconsistencies (in which case the different uses ofthe terms should be considered independently of each other).

BRIEF DESCRIPTION OF THE DRAWINGS

Various features, aspects, and advantages of the present disclosure willnow be described with reference to the drawings of embodiments, whichembodiments are intended to illustrate and not to limit the disclosure.

FIG. 1 is a flowchart that shows a method for performing manualvoxelation.

FIG. 2A shows a mid-sagittal 2-dimensional MRI image of anintervertebral disc with a voxel manually applied thereto.

FIG. 2B shows a mid-coronal 2-dimensional MRI image of an intervertebraldisc with a voxel manually applied thereto.

FIG. 2C shows an axial or oblique axial 2-dimensional MRI image of anintervertebral disc with a voxel manually applied thereto.

FIG. 3A shows a mid-sagittal 2-dimensional MRI image of anintervertebral disc with a mis-prescribed voxel applied thereto.

FIG. 3B shows a mid-coronal 2-dimensional MRI image of an intervertebraldisc with a mis-prescribed voxel applied thereto.

FIG. 3C shows an axial or oblique axial 2-dimensional MRI image of anintervertebral disc with a mis-prescribed voxel applied thereto.

FIG. 3D shows an MRS spectrum resulting from the mis-prescribed voxel ofFIGS. 3A-C.

FIG. 4A shows a mid-sagittal 2-dimensional MRI image of anintervertebral disc with a correctly prescribed voxel applied thereto.

FIG. 4B shows a mid-coronal 2-dimensional MRI image of an intervertebraldisc with a correctly prescribed voxel applied thereto.

FIG. 4C shows an axial or oblique axial 2-dimensional MRI image of anintervertebral disc with a correctly prescribed voxel applied thereto.

FIG. 4D shows an MRS spectrum resulting from the correctly prescribedvoxel of FIGS. 4A-C.

FIG. 5 is a flow diagram for an automated voxelation method.

FIG. 6 shows a mid-sagittal 2D planar MRI image of a spine.

FIG. 7A is a flow diagram showing the image processing portions of theflowchart of FIG. 5.

FIG. 7B is the mid-sagittal MRI image of FIG. 6 after the performance ofcertain image processing portions.

FIG. 8A is a flow diagram showing the ROI location and isolationportions of the flowchart of FIG. 5.

FIG. 8B is the mid-sagittal MRI image of FIG. 6 with increased contrastto highlight the intervertebral discs and showing default ROI areas.

FIG. 8C is the mid-sagittal MRI image with ellipsoids surrounding theautomatically identified disc locations.

FIG. 9A is a flow diagram showing the 2D voxel formation portions of theflowchart of FIG. 5.

FIG. 9B is the mid-sagittal MRI image showing 2D voxel prescriptionswithin the respective discs of the 5 lumbar disc levels.

FIG. 10A is a flow diagram showing the 3D voxel formation portions ofthe flowchart of FIG. 5.

FIG. 10B shows a 3D voxel relative to two transverse mid-sagittal andmid-coronal 2D MRI images.

FIG. 11A shows a T1-weighted image from a commercially available 3T MRIscanner of a lumbar spine segment in a volunteer human subject.

FIG. 11B shows a T2-weighted image from the same scanner of the samelumbar spine segment in same subject, and acquired during a differentpulse sequence acquisition segment of the same scanning session, as theT1-weighted image shown in FIG. 11A.

FIG. 11C shows a T1-T2 differencing image derived from the T1 andT2-weighted images of FIGS. 11A-B, respectively.

FIG. 11D shows a T1-T2 “blended” or “merged” image derived from the T1and T2-weighted images of FIGS. 11A-B.

FIG. 11E shows the T1-T2 differencing image of 11C at a first contrastsetting.

FIG. 11F shows the T1-T2 differencing image of 11C at a second contrastsetting that is different than the first contrast setting of theembodiment shown in FIG. 11E.

FIG. 12 shows an example embodiment of a system for performingvoxelation or for otherwise analyzing a ROI in one or more MRI images.

FIG. 13 is a flow chart illustrating example embodiments of methods forobtaining information relating to a ROI in one or more MRI images.

DETAILED DESCRIPTION OF SOME EMBODIMENTS

Various embodiments disclosed in the present disclosure relate toclinical magnetic resonance spectroscopy (MRS), in particular singlevoxel MRS, and still more particularly to the automated prescription ofvoxels within regions of interest where MRS is to be performed, such as,for example, intervertebral discs. One example application of thepresent disclosure is to single voxel MRS of the intervertebral discs ofthe lumbar spine, such as the three to five lower lumbar discs, such asfor the purpose of diagnosing disc degeneration or discogenic pain. Thesystems and methods described herein can be used in the application ofMRS to diagnose chronic, severe discogenic low back pain by identifyingdiscs that are more likely to be painful versus non-painful thoughchemical signature analysis of disc tissue.

Magnetic resonance imaging (MRI), and in particular magnetic resonancespectroscopy (MRS), uses a combination of radiofrequency (RF) pulses anddynamic magnetic fields to energize a target volume of materialresulting in the generation of resonant frequencies that arecharacteristic of the chemical constituents within the energized volume.In a clinical MRS application, the target volume or three dimensional(3D) volume is referred to as a voxel (e.g., a single point or pixelwith volume). As used herein the term voxel can refer to the threedimensional target volume. In some embodiments, a two dimensional voxelshape can be a slice or cross section of the three dimensional voxelvolume. Although various embodiments are disclosed herein in connectionwith forming a voxel for a three dimensional volume, it will beunderstood that the embodiments also relate to forming two dimensionalshape associated with a two dimensional portion of the ROI. In someembodiments, information identifying a ROI in only a two dimensionalarea can be useful. In clinical MRS, voxels are typically defined orprescribed using the sagittal, coronal, and axial or oblique axial twodimensional (2D) MRI images that include the region of interest (“ROI”).Using a graphical interface, the MRS technician manually draws or enterscoordinates for a pattern (typically a rectangle) in each of the threeplane images, for example according to various present embodiments tooutline the nucleus of a lumbar disc. The graphical interface convertsthe 2D patterns in multiple transverse (in some cases orthogonal) planesinto a 3D volume or voxel.

As a prescribed MRS voxel defines the volume of interest (“VOI”) whereMRS signatures are taken, its location and tissue defined thereindefines what signatures are captured. If tissue within the ROI isdistinctive from tissue bordering the ROI, mis-prescription of the voxelin terms of size and/or location to extend beyond the ROI could confoundMRS results by capturing unwanted signal from the bordering tissueunintended to be “voxelated” for the MRS signature acquisition.Furthermore, if it is not readily apparent that the voxel wasmis-prescribed, then the MRS spectrum resulting from the mis-prescribedacquisition and including bordering tissue signatures could bemisinterpreted to represent the intended tissue within the target ROIitself. If signatures from bordering tissues are not related to“biomarker” chemicals of interest for a particular diagnosis, this maybe less concerning in such circumstances. However, if signatures frombordering tissues relate directly or indirectly (such as by overlappingMRS signature regions between different chemicals) to such biomarkersignals being targeted for MRS analysis of the ROI, mis-prescription ofthe voxel could lead to mis-diagnosis of the tissue within the intendedROI.

Still further, aside from bordering tissue issues, voxel volume willtypically directly correlate with signal strength. A general goal ofmost MRS voxel prescriptions is thus to maximize voxel volume within adesired ROI for a tissue, so as to increase the signal strength, whilestill excluding bordering tissues or structures of potentially differentchemical composition relevant to MRS spectral acquisitions from the ROI.

In many applications, a rectilinear voxel may fit easily into a largeROI. This may be the case for example in many cases involved inapplications for the brain or breast where the voxel volume is a smallpercentage of the ROI volume. However in many certain otherapplications, such as intervertebral discs, the ROI may be rather smalland non-rectilinear and may consist of compound surfaces (e.g., havingdifferent curvature and/or shape at different portions of the perimeterof the ROI, such as nodules, which in some cases do not correspond to asmooth geometric equation or curve), such as resulting in a moreellipsoidal type shape (e.g. intervertebral discs). As such, manualprescription of one shape inside of another may not be readilyoptimized. A rectangle that is fitted for a particular planar slicethrough an ellipsoid ROI may not fit in the ROI of an adjacent slice,resulting in the voxel dimensions exceeding the shape of the ROI. Thisis one illustrative example of a mis-prescription that would bedesirable to avoid. To avoid a voxel mis-prescription, the MRStechnician typically verifies that the voxel prescribed in one set of 2Dimages fits within the adjacent 2D images or slices that encompass thevoxel. Even for skilled technicians, this can be challenging to getcorrect in many cases. Also, the trade off between maximizing voxelvolume for optimal signal-to-noise (SNR) ratio, versus excludingbordering different tissue structures, is an exercise in “risk-reward”trade-offs that may be challenging to optimize for a particular case(especially challenging cases, where the ROI is small and borderingstructures can introduce significant error if captured, as is the casefor intervertebral discs).

In the particular case of a voxel intended to capture an intervertebraldisc for MRS chemical signature analysis, and more specifically thenucleus of an intervertebral disc, this structure can be bordered onsome or all sides by different structures that are chemically distinctfrom the intended disc tissue. This is in particular the case superiorly(vertically above) and inferiorly (below) the disc, where vertebral bodyend-plates reside. These structures involve, among other constituents,bone marrow which are rich in lipid that has an MRS signature thatoverlaps with and may mask other target chemicals (such as for examplelactic acid and alanine). A mis-prescribed voxel for a disc nucleus MRSexam thus may contain tissue from the vertebral endplate adjacent to thenucleus resulting in resonant frequencies from both tissues. The MRSsignatures (e.g., lipid) from tissue surrounding the ROI can compromisethe ability to assess overlapping chemicals of the ROI (e.g., lacticacid and alanine), in addition to simply representing the wrong tissue.Additionally, in some cases, a degenerative painful disc itself maycontain lipid. To the extent a lipid signal is representative only ofbordering bone or end-plates and not the intervertebral disc, a lipidsignature in a resulting acquired spectrum could indicate amis-prescription and motivate a re-prescription in a repeat scan, suchas by moving or shrinking the voxel. However, because lipids andassociated MRS signatures can be present both in some actual disctissues, and in a mis-prescription involving bordering vertebral bodiesor the end-plates, the ability to recognize whether a mis-prescriptionoccurred based on a lipid signal may be compromised in some cases. Thus,in some cases, the identification of a lipid signature in an acquiredspectrum is not useful as an indicator of voxel mis-prescription, whilein other cases a lipid signature may be indicative of a voxelmis-prescription. In some embodiments, detection of a lipid signal overa threshold level can cause an MRS acquisition to be repeated (with avoxel of the same or different size), disregarded, flagged for userreview, and/or further analyzed for accuracy.

Further to the issues elsewhere noted surrounding manual voxelprescriptions, the process of defining single voxels may be very timeconsuming. For example, in a single voxel MRS exam of three lower lumbardiscs, if each voxel prescription took from about 2.5 to about 5minutes, that represents between about 7.5 to about 15 minutes totaltime for voxelation during the exam. For an actual pulse sequenceacquisition that may take for example about 2.5 to about 5 minutes, or7.5 to about 15 minutes total “scan time,” that manual voxelationessentially doubles the time of the exam (not accounting for otheractivities, such as imaging sequences, shimming, etc.). Wheremis-prescriptions occur and are caught, rescanning can further increasethe time of the exam.

Time means money in imaging, and healthcare costs have become among themost prominent issues in all of modern society world-wide. At least nearthe top of such issues reside the rising costs of imaging. Moreover,time that a patient resides in an MRI environment for an exam, or “inthe tube,” is limited due to patient comfort and other concerns. Overextended exam times, patients will often become more restless, thus morelikely to move, and movement can confound an MR exam (e.g. patientmovement after a voxel prescription can move the patient's tissues whilethe voxel location remains fixed in MR machine coordinates, effectivelycreating a voxel mis-prescription). Furthermore, patients such as lowback pain patients may be in tremendous discomfort to begin with. If themanual process were replaced by one that was at least partiallyautomated, significant time could be saved. For illustration, byreference to the example immediately above, replacing manual voxelationwith a fully automated process could potentially cut the time of theexam by as much as 25-50%.

Manual voxelation thus represents a human operator-dependent process,and the results of the MRS exam can directly (and in some casescritically) depend on correct performance of the manual voxelation.Thus, manual voxelation introduces the risk of human error. When therisk of human error is high, and the impacts of the potential error canbe critical, the need to find a solution to remove this potentialopportunity and source for human error becomes that much more important.This does not necessarily reflect badly on the human operatorsthemselves. Manual voxelation for single voxel spectroscopy may be verydifficult to get right, especially in challenging MRS applications insmall defined tissue regions. Inter-operator variability could be higheven between the most highly skilled and diligent of operators,especially in particularly challenging cases. Thus, an automatedvoxelation process can reduce or eliminate the occurrence of human errorand can increase consistency and predictability in the voxelationprocess and in the MRS results.

Accordingly, one aspect of this disclosure provides a useful solution toreplace, or at least augment, manual voxel prescription (e.g., forsingle voxel MRS exams) by an at least partially automated voxelationsystem and method. In fact, in some embodiments herein described, thevoxelation is either fully or nearly fully automated. According to oneparticular benefit, this can mitigate certain associated potential risksand issues that may impact single voxel MRS exams. According to anotherbenefit, it shortens the time necessary for the voxelation portion ofthe exam, which shortens the time for the overall exam, which may resultin more efficient delivery of healthcare, more patient comfort, and morerobust results.

In particular, one mode of this aspect provides a system and method toautomate the voxel prescription process by identifying the boundaries ofthe ROI in a set of 2D images that encompass the ROI, creating a 3Dmodel of the ROI, and fitting a rectilinear shape/voxel within the ROI.The resulting dimensions and coordinates of the voxel are then presentedto the MRS technician for entry into the graphical user interface. Thesystem may also process/define multiple voxels for multiple ROI within acommon field of view (FOV) simultaneously. Thus, although manyembodiments are described herein in connection with single voxel MRS,the systems and methods may also be applied to multivoxel MRS (e.g., formultiple ROI). In the example of lower lumbar applications, voxels maybe automatically prescribed for multiple (e.g., all five) lower lumbardiscs.

Some embodiments of the present disclosure can be used in automating thevoxel prescription of an intervertebral disc, such as for MRS exams toprovide diagnostic information related to disc degeneration and/ordiscogenic pain.

FIG. 1 shows the flow process of an MRS session 10 that uses manualvoxel prescription. The process is illustrated by blocks 11-15 shown inFIG. 1 as described in further detail below. Detailed numbers such asfor dimensions etc., as indicated immediately below and elsewhere hereinthis disclosure, are provided in order to describe specific illustrativeembodiments only, and are provided as “about” approximations, and mayvary from such specified values as apparent to one of ordinary skill.

At block 11, sagittal, coronal, and axial or oblique axial MRI imagesets can be captured. For example, a series (e.g., 13 slices) of highresolution T2-w MRI images of the ROI in the sagittal plane can becaptured using a field of view (FOV) of 40 cm×40 cm and a 4 mm slicethickness. A series (e.g., 13 slices) of high resolution T2-w MRI imagesof the ROI in the coronal plane can be captured using a field of view(FOV) of 40 cm×40 cm and a 4 mm slice thickness. A single highresolution T2-w MRI image of the ROI in the axial or axial oblique plane(e.g., typically more angulated at the L5/S1 level) can be capturedusing a field of view (FOV) of 40 cm×40 cm and a 4 mm slice thickness.It will be understood that MRI images may be produced using variousother suitable parameters. Also, in some embodiments, a series ofmultiple MRI images can be used in the axial or oblique axial planeinstead of a single MRI image, and the series of sagittal and coronalMRI images may contain more or fewer than the 13 images described above.

At block 12, the MRI image of the slice that intersects the center ofthe ROI in the sagittal plane can be identified. Typically, the MRIimage having the largest cross-sectional area of the ROI is the slicethat intersects the center of the ROI. In a series of 13 MRI images asdescribed above, slice number 7 or 8 will typically intersect the centerof the ROI. In some embodiments, this process may be repeated for theMRI images in the coronal plane, to identify the MRI image of the slicethat intersects the center of the ROI in the coronal plane. If a seriesof MRI images are used in the axial or oblique axial plane, an MRI imagethat intersects the center of the ROI in the axial or oblique axialplane can also be identified. If the axial or oblique axial MRI imageset includes only a single image, that single MRI image can be used asthe center image.

At block 13, a rectangle can be drawn using the MRI graphical userinterface in each of the three planes. For each axis, if an MRI imagewas identified in block 12 as intersecting the center of the ROI (e.g.,having the largest cross-sectional area), that MRI image can be used todraw the rectangle for that axis.

At block 14, the system can present the adjacent MRI images from the MRIimage sets with the drawn rectangle projected onto the image. The usercan observe the projected rectangle in some or all of the MRI images inthe sagittal MRI image set to verify that the rectangle fits inside theROI for each slice in the sagittal orientation. The user can observe theprojected rectangle in some or all of the MRI images in the coronal MRIimage set to verify that the rectangle fits inside the ROI for eachslice in the coronal orientation. If multiple images are used in theaxial or axial oblique angle, the user can observe the projectedrectangle in some or all of the MRI images in the axial or oblique axialMRI image set to verify that the rectangle fits inside the ROI for eachslice in the axial or oblique axial orientation.

At block 15, if needed, the user can adjust the dimensions, coordinates,and angle of the rectangle so as to keep the rectangle within the ROI.It will be understood that in some embodiments, block 15 can be omitted,for example, if no adjustments to the rectangle are needed after theinitial rectangle designation. In some cases, the user may reduce thesize of the rectangle so as to exclude area outside the region ofinterest. The user may also increase the size of the rectangle so as tocapture more volume of the region of interest. The user may also changethe angle or orientation of the rectangle so as to better fit the volumeof the ROI.

If the rectangle is adjusted in block 15, the process can repeat block14 to confirm that the adjusted rectangle fits into the ROI in some orall of the slices of the MRI images sets. In some embodiments, the usercan repeat blocks 14 and 15 (multiple times if needed) until a rectangleis defined that covers a large portion of the ROI but does not extendoutside the ROI. In some embodiments, the user may first adjust therectangle in the sagittal and coronal directions and once satisfied withtheir positioning can confirm in the single axial or oblique axial planeMRI image that the rectangular dimensions are contained within the ROIin the axial or oblique axial direction.

Once the user is satisfied with the rectangle dimensions and angles, theprocess can proceed to block 20 and perform the MRS scan. Blocks 11through 20 can then be repeated for additional ROIs (e.g., additionalintervertebral discs) to be scanned.

FIGS. 2A-C show mid-sagittal (FIG. 2A), mid-coronal (FIG. 2B), and axialor oblique axial (FIG. 2C) 2-dimensional (2D) planar views of MRI imagestaken during imaging phase of one illustrative MRS exam of a humansubject's spine 30, including a disc 40 bordered by superior andinferior end-plates 45, 46 between disc 40 and superior and inferiorvertebral bodies 50, 60 located above and below disc 40. The disc 40 caninclude a disc nucleus 42 and a disc annulus 44. Shown superimposed onthese planar MRI images is a rectangular voxel 70 drawn per a manualprescription as described above.

An example of the potential risks associated with manual prescription isillustrated by reference to FIGS. 3A-3D and 4A-4D, which illustrateanother MRS exam of another subject where one MRS exam conductedaccording to a mis-prescribed voxel (FIGS. 3A-3D) is compared againstthe MRS results of another MRS exam conducted with a more appropriateprescription (FIGS. 4A-4D).

More specifically, FIGS. 3A-3C show mid-sagittal (FIG. 3A), mid-coronal(FIG. 3B), and axial or oblique axial (FIG. 3C) 2-dimensional (2D)planar views of MRI images taken during imaging phase of an illustrativeMRS exam of a human subject's spine 100, including a disc 110 borderedby superior and inferior end-plates 112, 113 between disc 110 andsuperior and inferior vertebral bodies 120, 130 located above and belowdisc 110. Shown superimposed on these planar MRI images is a rectangularvoxel 140 drawn per a manual prescription as described above. The voxel140 as shown in FIG. 3A potentially impinges on one or both ofend-plates 112, 113. An MRS exam per this manual voxel prescription,conducted according to an MRS pulse sequence and post-processing systemand method similar to that described in PCT Patent Publication No. WO2011/047197, produced the MRS spectrum 150 shown in FIG. 3D. MRSspectrum 150 includes a relatively narrow and distinctive n-acetylaspartate (NAA) peak 152 typically representative of proteoglycan (PG)in disc nucleus tissues, and a still stronger peak 154 with much widerline width (e.g. broad between opposite sides of the peak, such as at50% power of peak) that spans across spectral regions 156, 158associated with lactic acid and alanine, respectively. This strong,broad peak region 154 is characteristic of lipid signal. If and to theextent any lactic acid or alanine signal may or may not contribute tothe signal intensity in this region is difficult to ascertain.

In contrast, FIGS. 4A-4C show similar images for the same spine 100 inthe same subject, and in fact during the same MR study session, but fora different MRS exam scan according to a different manual voxelprescription shown at voxel 145. As compared against voxel 140 shown inthe prior manually prescribed exam of FIGS. 3A-C, voxel 145 has slightlyreduced height vertically across disc 110 and potentially excludesend-plates 112, 113, either of which may have been partially captured bythe larger voxel 140 with the larger vertical height dimension in theprior exam. The MRS spectrum 162 acquired and processed for this voxelprescription, according to similar MRS pulse sequence andpost-processing methods as reflected in the spectral results shown inFIG. 3D for the prior exam, is shown in FIG. 4D. TheProteoglycan-related n-acetyl aspartate (NAA) peak 162 for spectrum 160is slightly reduced signal intensity than the similar peak 152 shown inFIG. 3D (between about 8 to 9×10⁸ for peak 162 versus about 12×10⁸ forpeak 152), as would be expected from a smaller voxel in the second case.However, the overall signal quality is apparent to be much more robust,as the lactate-related and alanine-related spectral ranges 166, 168include only a slight peak 164, possibly related to small level oflactic acid, but the spectrum 160 appears to be substantially devoid ofprominent lipid. By comparison of this first and second voxelprescription results, lipid peak 154 appears to have been the result ofcaptured end-plate contaminant in the MRS spectrum 150 of the“over-prescribed” voxel dimensions in that case.

Example embodiments relating to automated voxelation methods areprovided below by reference to FIGS. 5-10B. More specifically, FIG. 5shows a software flow diagram 200 of an automated voxelation method thatcan be used by an automated voxelation system, such as the system ofFIG. 11 discussed below. As discussed in more detail below, the systemcan include an MRI/MRS system configured to acquire MRI images of aregion of interest in a portion of a patient's body and/or to perform anMRS exam or procedure using the voxelation. The system can also includea computer system that can have a processor and a computer readablemedium, which can be configured to execute a program that performs someor all of the method shown in FIG. 5. The method of FIG. 5 can include:image processing 210, including further detailed blocks 211-214 shown;ROI location and isolation 220, including further detailed blocks221-224 shown; 2D voxel formation 230, including further detailed blocks231-233 shown; and 3D voxel formation 240, including further detailedblocks 241-246 shown. Various aspects of the method are furtherillustrated in additional FIGS. 6-10B as follows.

FIG. 6 shows a mid-sagittal 2D planar MRI image 250 of the same spineillustrated in the examples of manual voxel prescription shown anddescribed above by reference to FIGS. 3A-4D, but prior to an automatedvoxel prescription process according to some embodiments.

FIG. 7A shows a flow diagram which reintroduces various detailed blocks211-214 of image processing phase 210 of the automated voxelationapproach illustrated above in FIG. 5. The modified mid-sagittal 2Dplanar image 250 shown in FIG. 7B illustrates the results from the imageprocessing described.

FIG. 8A reintroduces the flow diagram from FIG. 5 for a ROI location andisolation phase 220 of the automated voxelation program 200, includingdetailed blocks 221-224. FIG. 8B shows a mid-sagittal 2D planar MRIimage 250 as in prior figures for the exemplary spine, but as modifiedto reflect the results arrived at following completion of the locationof a unique population of pixels for the ROI at block 222 shown in FIG.8A. More specifically, FIG. 8B shows default ellipsoid search areas 260applied to the image 250, with the unique population of pixels estimatedto represent the disc ROI shown at contrast enhanced brightpseudo-ellipsoidal regions 270 within default ellipsoid search areas260. FIG. 8C shows the same MRI image 250 as prior FIGS. 6, 7B, and 8Babove, but as further modified to reflect and plot the estimatedellipsoid shapes 280 generated by the program for the respective discnucleus regions at block 224 shown in FIG. 8A.

FIG. 9A reintroduces flow diagram 200 with respect to the 2D voxelformation phase 230, including more detailed blocks 231-233 in order toprepare for 3D voxel formation phase 240. An illustrative result of thisphase is shown in FIG. 9B, which shows the mid-saggital 2D planar image250 with the 2D voxel prescriptions 290 shown within the respectivediscs of the 5 lumbar disc levels. FIG. 9B also shows the 2D planarimage 252 for the orthogonal transverse oblique axial plane for theL4-L5 disc in this example.

FIGS. 10A-B illustrate the result of this process as follows. FIG. 10Areintroduces for flow diagram 200 the flow for 3D voxel formation phase240, including more detailed blocks thereof 241-246. An illustrative 3Dvoxel prescription result is shown for one disc of the spine in angularperspective view relative to two transverse mid-sagittal (e.g., yzplane) and mid-coronal (e.g., xz plane) 2D MRI images.

Additional details relating to various aspects of the process 200described above are provided below. It is generally to be appreciatedthat the current systems and methods may be employed using a variety ofdifferent types of resources, including software programs, utilities,etc. According to certain detailed present embodiments, a softwareutility program “MATLAB” can be used. More specifically, the specificexamples provided for detailed illustration in some embodiments havebeen put into actual use using the R2010b version of MATLAB along withthe MATLAB Statistics and Image Processing toolboxes within thatversion.

At block 211, the process locates an MRI image, which can be a sagittalMRI image through the center of the ROI (e.g., having the largest ROIcross-sectional area). In some embodiments, the automated voxelation canuse the same working set of MRI images used in manual voxel prescriptionprocess. Prior to image use, in some embodiments, the automatedvoxelation system and method can access the image files in each seriesand form a volume image structure for each consisting of a stack ofimage arrays.

In some embodiments, the method for collecting the sagittal, coronal,and axial or oblique axial image sets can be performed automatically orcan be partially automatic. In some embodiments, a user can identify amid-sagittal location for the mid-sagittal image (e.g., by selecting oneof a series of low resolution images that corresponds to a sagittalcenter position), and the system can automatically compile a series ofhigh resolution MRI images including a mid-sagittal MRI image at thelocation indicated by the user as well as other sagittal images taken atplanes substantially parallel to, and space from, the mid-sagittalimage. In some embodiments, some or all of the image processing phase210 and ROI location and isolation phase 220 can be performed on aplurality of MRI images before a center slice is identified, so that acenter MRI image can be identified by automatically comparing the sizesof the ROI in the plurality of MRI images. For example, the system canaccess a plurality of MRI images taken from substantially parallelplanes that intersect the ROI, can calculate an area of the ROI in theplurality of MRI images, and can use the image with the largest ROI areaas the center slice for voxel positioning. When multiple ROI (e.g.,multiple intervertebral discs) are being analyzed, the system cancalculate the ROI area for the multiple ROI in the plurality of MRIimages and can select a center MRI image having the largest aggregateROI area combined from the multiple ROI. This approach can bebeneficial, for example, when identifying multiple voxels, e.g., formultivoxel MRS procedures with multiple ROI. In some embodiments, acenter MRI image can be selected based on the largest ROI area for asingle ROI, and in some cases, the system can allow for different MRIimages to be selected as the center MRI image for different ROI. Thisapproach can be beneficial, for example, for single voxel MRSprocedures. The system can access multiple MRI images (e.g., taken fromsubstantially parallel planes), and can test all or a subset of theimages (e.g., the middle 7 images of an array of 13 image slices) toidentify a center image. The system can start with an image at themiddle of a series of images and can measure the ROI area for a numberof images (e.g., 3 images) on either side of the middle image in theseries of images.

In some embodiments, the selected center image can be an image otherthan the middle image of the series of MRI images, and the selectedcenter image can, in some cases, correspond to a slice that is notthrough a center of the disc, or spine, or patient's body. A centerimage can be the middle image of a series of MRI image slices, and can,in some cases, pass through substantially the center of the ROI, thedisc, the spine, and/or the patient's body. Many other variations arepossible. In some embodiments, the system can identify the MRI imagesthat have an ROI interest that meets a threshold amount, and can definethe center MRI image to be the image at the middle of the series ofimages that meet the threshold FOI area amount. In some embodiments, thesystem can omit the identification of a center image. For example, thesystem can use some or all of the MRI images to generate a 3D model ofthe ROI without identifying a center image for the ROI. A voxel can beformed based on the 3D model of the ROI without identification of acenter image for the ROI.

In some embodiment, the center image identification process can berepeated for the coronal series and axial or oblique axial series of MRIimages in the coronal and axial or oblique axial planes. In someembodiments, the voxelation method 200 can use MRI images taken fromonly a single axis (e.g., the sagittal axis) for prescribing a voxel forthe ROI. Thus, in some embodiments, the images of one or both of theother two axes (e.g., the coronal and/or the axial images) can beomitted. In some embodiments, the system can locate the ROI and/orpositioned the voxel based on images from a single axis (e.g., sagittal)and the system can use one or more images from one or both of the otheraxes (e.g., coronal and/or axial) for displaying information to theuser, such as for showing a 3D voxel in a 3D presentation betweenmultiple images of different planes, as shown in FIG. 10B and discussedbelow.

In some embodiments, as part of the image processing 210, the system canconvert the MRI image to a different format, such as from DICOM (DigitalImaging and Communications in Medicine) image data to MATLAB “gray”format with double precision intensity values in the range from 0 to 1.Portions of the MRI image can correspond to the ROI and surrounding areain a physical target object (e.g., a patient's spine). Although manyembodiments disclosed herein are described in connection with MRIimages, it will be understood that various types of electronic imagescan be used. An electronic image can be any electronic representation ofan image, and may be related to MRI, CT, PET, X-ray, or other modality.The electronic image can include an image coordinate system and aplurality of pixels with unique respective image coordinates. Suchelectronic images may be considered “acquired” from a subject by virtueof the respective imaging modality that gathers information from thebody which is then converted to the electronic image. An electronicimage may comprise for example a single 2D planar image with x-y, x-z,or y-z coordinates, or a series of related 2D images in different planesthat provide a coordinated “picture” of a region, such as may becombined in an array of images to provide a 3D image. Thus, throughoutthis disclosure, various types of electronic images can be used in placeof the MRI images that are specifically discussed. The system can accessmetadata from the image files to support 3D interpretation of theimage-coordinate data in 3D machine coordinates, also referred to asworld coordinates for current purposes of this disclosure. For example,the system can form 4×4 homogeneous coordinate transformation matricesfrom the metadata to support image to machine coordinate transformation.Thus, images coordinate data corresponding to portions of the MRI imagecan be converted or transformed into world coordinates corresponding toportions of the target object. In some embodiments, the system can thenorganize all the above data into an AutoVox data structure that containsall the information to create a 3D volume display in machine (or world)coordinates. One example of a 3D volume display is shown in FIG. 10B,which is discussed below.

In order to analyze an MRI image, image processing can be performed tosmooth the image, level the intensity variation from anterior toposterior, remove the bright spinal column and posterior fat signal, andfinally emphasize the ROI (e.g., disc nucleus). The result of imageprocessing 210 can be an image consisting almost exclusively ofemphasized sub-images of multiple ROIs, as shown in FIG. 7B.

Various types of image smoothing can be used at block 212. For example,the system can perform 3D smoothing using the MATLAB function “smooth3”with a 3×3×3 cubic kernel. This 3D smoothing algorithm accesses theselected sagittal image and the sagittal images which bracket it in theimage stack. This algorithm was found to provide notably bettersmoothing than a 2D algorithm with the 3D smoothing algorithm producingessentially no loss of edge acuity. Thus, in some embodiments an imagesmoothing operation 212 can modify the brightness of a pixel based onthe brightness values of neighboring pixels, for example setting thepixel brightness to an average value, or a weighted average value, etc.In some embodiments, the neighboring pixels can be part of the sameimage (e.g., a 3×3 or 5×5 area around the pixel) and/or can be part ofother images in the series of substantially parallel images (e.g.,sagittal MRI images). For example, in the 3×3×3 kernel mentioned above,can use the adjacent image on both sides of the image containing thepixel being processed. The kernel can be referred to as cubic becausethe number of pixels that are used to modify the brightness of a pixelare equal (e.g., 3) in each direction (e.g., x, y, and z), even thoughthe physical volume may not form a cube. For example, in the 3×3×3 cubickernel example, the distance between adjacent MRI slices can be largerthan the distance between pixels within the same image resulting in thecubic kernel coving a somewhat elongated rectilinear volume rather thana cube.

At block 213, top-hat filtering can be performed on the MRI image. Thetop-hat filtering can be configured to deemphasize (e.g., darken orremove) portions of the image that correspond to features other than theROI while preserving the ROI portions of the image. In the example ofintervertebral discs, the spinal column and posterior fat can formbright portions of the image (e.g., due to high water content), and thetop-hat filtering operation can be configured to deemphasize thesefeatures while preserving the disc portions of the image. The system canperform, for example, morphological top-hat filtering on the image usingvertical linear structuring element 50 pixels tall and 5 pixels wide.The structuring element can be effectively 50 running averages with 5data points per average that is shifted across the image. Thestructuring element can be generally shaped like the shape of thestructures to be deemphasized. As the structuring element is sweptacross the image, if the pixels covered by the structuring elementsatisfy a criteria (e.g., a threshold brightness level) then the pixelscovered by the structuring element can be deemphasized (e.g., darkenedor removed). In the illustrated example, the structuring element can begenerally tall and thin (e.g., 50 pixels tall and 5 pixels wide) so thatthe structuring element can fit into the vertically oriented spinalcolumn and fat portions of the image and not fit into the intervertebraldisc portions of the image which are generally ellipsoidal in shape andrelatively short and wide. Thus, the top-hat filtering can primarilytarget removing the bright water signal of the spinal canal andminimizing the signal from the posterior fat. The top-hat filtering canprimarily deconstruct the bright water-rich image of the spinal canal inthe MRI image to facilitate the algorithm that searches for the bright(e.g., water-rich) oval shape associated with the disc nucleus. It alsocan have the effect of leveling the intensity of the image. The top-hatfiltering operation can basically remove (or darken) any parts of theimage which it can encompass by the structuring element. Its dimensionsare selected so it does not affect the ROI (e.g., disc nucleus) images.

In some embodiments, a first top-hat operation does not effectivelyremove the signal from the lower curved portion of the spinal canal,typically below L4L5. The system can then perform a second top-hatfiltering operation with the structuring element as a line 50 pixelslong and at a 45 degree slope to target the sloping lower portion of thespinal canal to target the lower curved portion of the spinal canal.Many variations are possible. Many different configurations of top-hatfiltering operations can be performed depending on the shape andstructure of the ROI and the surrounding area, and multiple top-hatfiltering operations of various different numbers can be performed. Insome embodiments, other forms of morphological image processingoperations can be performed to either emphasize the ROI or todeemphasize the regions of the image not associated with the ROI.

At block 214, the system can perform order statistic filtering tofurther smooth and level the image while preserving edges. In someembodiments, two dimensional (2D) order statistic filtering can be used.The kernel, or domain, for the order statistic filtering can be a 5 by 5pixel square, although other sizes can be used. The order statisticfiltering can set the brightness of a pixel based on the brightness ofneighboring pixels. In some embodiments the sixth order can be used,although other orders can be used. For example, in the sixth orderembodiment, the filter operation can order the neighboring pixels andthe analyzed area (e.g., 25 pixels in the 5×5 example) from darkest tobrightest, and the filter operation can set the brightness value of theanalyzed pixel to the sixth brightness value from the darkest. Thus, theorder statistic filtering operation can generally darken the imageexcept for pixels of the image that are generally surrounded by otherbright pixels. The 5×5 kernel and sixth order parameters were optimizedexperimentally for some applications relating to intervertebral discROI, but many other configurations (e.g., of size, shape, and/or orders)can be used depending on the application. This operation can serve toremove small speckles and fill small holes while generally preservingedges of the ROI.

Many variations to the disclosed image processing 210 can be made. Forexample, in some embodiments, one or more of the operations discussedherein can be omitted or combined with other operations. For example, insome embodiments, the method can use only one of 3D image smoothing 212and order statistic filtering 214 for removing noise, etc. and the otherof 3D image smoothing 212 and order statistic filtering 214 can beomitted. Also, as discussed elsewhere herein, in some embodiments, themethod can locate a center image 211 at a later stage of the method orthe locating of a center image 211 can be omitted entirely. Also,additional image processing operations can be added in combination withor in place of the operations illustrated in the image processing phase210 of FIG. 7A. For example, various other types of noise reductionfilters can be applied. Also, a contrast enhancing filter can be appliedto emphasize contrast in the image to facilitate identification of theedges of the ROI. In some embodiments, edge detection can be performedon the image to identify or emphasize the edges of the ROI. The methodsand systems disclosed herein can perform one or more of the operationsof the image processing phase alone or with various combinations of theother components of the method shown in FIG. 5 or described herein.

Having processed the image to emphasize the ROI (e.g, disc nuclei) whilepreserving their location, the system can next isolate and locate theROI (e.g., disc nuclei) at phase 220. At block 221, default search areas260 can be applied to the image. For example, in the illustratedembodiment, statistics on the location of each disc level in the imagehave been developed. For the default search areas 260 shown in FIG. 8B,a training set of ten MRI studies where digitized to locate disc centerand disc tilt. The means of the disc centroid and disc tilt were used todefine an ellipsoid search area for each level, such as shown at defaultellipsoid search areas 260 in FIG. 8B. For each level, the search areaellipse 260 has a semi-major axis of 60 pixels and a semi-minor axis of30 pixels and is centered on the mean location in image coordinates fora disc at that level, and the tilts for each level are 0°, 0°, −5°,−18°, and −30° for levels L1L2 to L5S1 respectively, although otherconfigurations can also be used. This approach is justified as thelumbar image prescription protocol is very well standardized andfollowed in the studies acquired to date. In some embodiments, thesystem may use active shape modeling to locate ROIs or operatorintervention to identify and/or confirm ROIs.

As would be apparent to one of ordinary skill based upon the disclosureimmediately above, this present illustrated embodiment thus provides atemplate for refining statistical methods to determine actual disclocations based upon an atlas applied to the image developed frompre-existing knowledge of typical locations derived from prioracquisitions and segmentation defining the disc locations (whichsegmentation may even be done manually for purpose of creating theatlas). The template provides default regions in which the various disclevels in spines previously used to construct the template were known tobe located, thus providing default regions applied to a given spineimage so that processing algorithms may more effectively narrow afocused statistical search for the actual discs within that one newspine image. In some embodiments a single default search area 260 can beapplied if a single ROI is to be identified, and in some embodiments, aplurality of default search areas 260 can be applied if multiple ROI tobe identified.

After applying default ellipsoid search areas 260 to the image, such asat block 221 of Phase 220 of the voxel automation 200, the following canbe performed. At block 222, for each or one or more of the ellipticalsearch areas 260, all the pixels within the area which have a particularproperty (e.g., an intensity of at least 0.15) can be assigned to apixel population corresponding to the ROI. The pixels can be identifiedby their spatial coordinates in the image (which correspond to worldcoordinates in the imaged object).

In some embodiments, the system can analyze the population of pixels 270based on a reliability criteria. One criteria that may be applied, forexample, is to require a certain threshold number, such as for exampleat least 300, of pixels in the pixel population (or grouped together) toqualify as a reliable estimate. In some embodiments, a tiered approachcan be used depending on the number of pixels detects as part of thepixel population. In some embodiments, if there are between 150 and 299pixels the pixel population may still be processed but considered, andflagged, as potentially unreliable; or, the pixel population may bekicked out of the automated system for manual prescription; or, furtheralgorithms may be employed specifically tailored to overcome suchcircumstance. If there are fewer than 150 pixels, in some embodiments,the system can be set such that no attempt is made to locate thecorresponding disc. Many variations and alternatives are possible. Thecomparison of the reliability criteria can reduce or avoid incorrectlykeying on residual image artifacts as though they were a ROI. Theaforementioned values have been empirically determined for a givenpopulation of examples, which is relatively small. Thus, values otherthan those specifically disclosed herein may be used in view of furtherdata of clinically relevant numbers representative of a given targetpopulation distribution. Also, values other than those specificallydisclosed herein may be used for other specific applications other thanthat described herein (e.g., voxelation in regions of the body otherthan intervertebral discs).

In this regard, it is to be appreciated that the default and estimatedaspects of these detailed embodiments applied may be influenced bycertain subject-dependent variables, such as height, weight, BMI,gender, age, race or ethnicity. The present disclosure contemplates thataspects of the present embodiments may take such variability intoaccount, such as for example generating default ellipsoid search areas260 from spine MRIs from target numbers of samples on such uniquelyidentifiable sub-population bases (e.g., based on age, gender, race,height, weight, or BMI).

FIG. 8B shows a bright spot 272 that appears structurally to be near butclearly outside of the L3-L4 disc nucleus, but is still nonethelesscaptured within that respectively assigned default ellipsoid search area260. The bright spot 272 is separated from the more heavily populatedpixilated region that does appear to be the respective disc nucleus.Such non-target tissue region artifact 272 may result in statistics usedin further processing to capture signal intensity from such peripheralstructures outside of the target disc nucleus, and thus potentiallycompromise the statistics used to estimate the disc nucleus. In fact,one possible result for this example is shown in FIG. 8C at theestimated ellipsoid shape 285 for that same L3-L4 disc. Relative to theother estimated disc nucleus ROI ellipsoids 280, the estimated shape 285is slightly off-angle and extended partially in the direction of theartifact signal 272, and appears to potentially capture some inferiorvertebral body structure.

Accordingly, some embodiments of this disclosure contemplate use offurther algorithms and methods to distinguish possible sources for suchpotential artifact, such as by statistical methods to exclude artifactsin one step from being used in calculations in the next step (e.g.,confidence interval calculations, basing on separation between strongestcontiguous signal region, location relative to the default ROI,combinations thereof, etc.). In some embodiments artifacts can beexcluded by review of the quality of the results 285 shown in FIG. 8Crelative to signal intensity, contrast, or image homogeneity capturedwithin the drawn shape. The results can be modified to correct or allowfor circular correction algorithms back to prior operations (butperformed differently based on data obtained from subsequentoperation(s)). In the example of FIG. 8C, a neighboring function can beperformed that identifies that the identified population of pixelsincludes a neighboring group of pixels near the main group of pixels,and the neighboring function can cause the neighboring group of pixelsto be excluded from analysis of the population of pixels. Theneighboring function can identify neighboring groups of pixels that donot contact and are separated from the main group of pixels. In someembodiments, the neighboring function can be configured to identify aneighboring group of pixels that do contact the main group of pixels byrecognizing that the neighboring group of pixels do not conform to anexpected shape of the ROI, for example, if the bright spot 272 of FIG.8B were connected to the bright ellipsoid disc shape as an arm orextension that does not fit with the ellipsoid shape.

In some embodiments, further manual opportunities may be provided forassistance. For example, the image 250 shown as modified in FIG. 8B maybe presented to a user to allow for regions to be manually captured(e.g. mouse drag) for exclusion or inclusion. Manual indications ofareas of exclusion or inclusion may be done as a matter of course or maybe done only when a particular interim result is “flagged” aspotentially erroneous and thus appropriate for manual intervention.Thus, in some embodiments, review of the process and/or results can beperformed in order to optimize results or to correct or remove sourcesof error.

At block 224, the system can calculate a two dimensional model from theshape of the population of pixels. In the embodiment illustrated in FIG.8C, for the cluster of pixel locations identified in each level searcharea, an expectation maximization algorithm for decomposing Gaussianmixtures (EM_GM algorithm) can be applied at block 223. The EM_GMalgorithm can provide parameters (e.g., means and covariance matrix) ofa two dimensional Gaussian distribution representing the spatialdistribution of the pixels in the cluster. A cross section or footprintof the 2D Gaussian distribution can have a generally ellipsoidal shape.With appropriate scaling, experimental observation indicates that theEM_GM algorithm appears to form a very good approximation to thegenerally ellipsoidal shape of the disc nucleus, although other methodscan be used to form a 2D shape or model based on the population ofpixels. In the illustrated embodiment, examining the eigenvectors of thecovariance matrix can be used to yield the orientation (tilt) of thedisc, which initial experimental observation has also suggested resultsin very good accuracy. An experimentally determined scale factor of 2.5can be applied to the standard deviations from the covariance matrix touse as the semi-major and semi-minor axes of the disc ellipse estimate.Various other shape approximation operations can be used to estimate theshape of the ROI based on the population of pixels identified at block222. For example, active shape modeling, statistical shape modeling, orvarious other techniques such as those generally referred to as blobdetection can be used to estimate the shape of the ROI.

Many variations to the disclosed ROI location and isolation process 220can be made. For example, in some embodiments, one or more of theoperations discussed herein can be omitted or combined with otheroperations. For example, in some embodiments, the image processing canemphasize the ROI so that the process can locate the populations ofpixels without using the applying the default search areas at block 221.In some embodiments, a default area (e.g., formed by atlas-basedsegmentation and/or historical date of common ROI locations) can be usedafter ROI detection or after voxel formation as a check. For example, ifthe location of an identified ROI, or prescribed voxel, does not fitinto or correspond with the default area, the identified ROI, orprescribed voxel, can be flagged as potential unreliable, disregarded,presented to the user for review, recalculated, and/or adjusted. Also,operations can be added in combination with, or in place of, theoperations illustrated in the method shown in FIG. 8A. For example, asmentioned above, various types of blob detection techniques can be usedto identify the shape of a ROI.

Also, in some embodiments, the method can use user input along withautomated procedures to identify the ROI. For example, the user can bepermitted to identify a location (e.g., by clicking on the image) thatis inside of the ROI (a single location for a single ROI or multiplelocations for multiple ROI) to provide guidance in locating the ROI. Forexample, the process can search within an area centered on the locationidentified by the user to search for the ROI boundaries. For example,the process can use an edge detection operation to identify edges of theROI around the location identified by the user. The program can also usethe image brightness of the location clicked by the user in determiningwhat brightness threshold to apply for identifying other pixelsassociated with the ROI. The contrast ratio between ROI and non-ROIportions of the image can vary significantly between patients, but ifthe user specifies a location that is known to be part of the ROI, thebrightness of that portion of the image can be used to set thebrightness threshold used to identify other portions of the same ROI orof other ROI. In some embodiments, the program can allow the user toidentify (e.g., click) points outside the ROI, such as along one or bothof the end plates that surround the intervertebral disc, and the programcan use those points to guide the locating of the end plates or of theROI (e.g., disc between the end plates). The program can select thevoxel size and/or orientation based at least in part on the clicked endplate points, e.g., by fitting a linear line or curve between theclicked end plate points and placing the voxel between the linesassociated with the end plates. The end plate clicks can be used todefine a search area for locating the ROI as well.

The methods and systems disclosed herein can perform one or more of theoperations of the ROI location and isolation phase 220 alone or withvarious combinations of the other components of the method shown in FIG.5 or described herein.

Given the above ROI (disc nucleus) characteristic data provided per theoperations above, it becomes possible to automatically form twodimensional (2D) shapes or voxels, at phase 230. At block 231, imagecoordinate data can be extracted, and at block 232, the imagecoordinates can be converted to world coordinates using a transformationmatrix. The system can, for example, apply an experimentally determinedscale factor (e.g., of 2.9 times the image to world scale factor (e.g.,0.39 from the DICOM metadata)) to the semi-minor axis to determine thevoxel thickness in the z-axis (axial axis), and to the semi-minor axisto determine the voxel dimensions in the machine y-axis (coronal axis),as shown in block 233. In some embodiments, the 2D shape can be arectilinear shape such as a rectangle, although many other 2D shapes canbe used. Many variations are possible. Various components of the 2Dvoxel formation phase 230 can be combined or omitted. For example, insome embodiments, the 2D shape can be defined in image coordinates andthen the coordinates of the 2D image can be transformed to machine (orworld) coordinates, or the transformation to world coordinates can beomitted from the 2D voxel formation phase 230, for example bytransforming image coordinates to world coordinates after the 3D voxelis formulated. The methods and systems disclosed herein can perform oneor more of the operations of the 2D voxel formation phase 230 alone orwith various combinations of the other components of the method shown inFIG. 5 or described herein.

3D Voxel Formation of phase 240 can include determining voxel widthacross the coronal plane or x-axis (sagittal axis), which can includethe following. The system can locate a sagittal slice adjacent to theprevious processed sagittal image slice at block 214, and can repeatimage processing 242, ROI location and isolation 243, and 2D voxelformation 244 for the sagittal slice adjacent to the previouslyprocessed sagittal image. The system can compare the newly calculated 2Dvoxel to the 2D voxel from the previously processed sagittal slice todetermine if the previously established 2D voxel dimensions fit withinthe current 2D voxel of the current sagittal slide being analyzed. Thesystem can modify (e.g., decrease) the 2D voxel dimensions if requiredto fit the current 2D voxel. This process can be repeated for eachsagittal slice being considered (e.g., until all sagittal slices thatencompass the ROI have been analyzed). A three dimensional (3D) volumeor voxel can be formed using the final 2D voxel dimensions as a crosssectional shape for the 3D voxel projected across the width of theanalyzed sagittal slices to form a rectilinear volume. Although manyembodiments are described herein as using a series of MRI imagescorresponding to sagittal slices, coronal, axial, or oblique axial MRIimages can also be used.

In some embodiments, the volume of the 3D voxel can be increased ormaximized while also being contained within the ROI. For example, for avoxel with a rectilinear volume shape and a ROI having a generallyellipsoidal shape (e.g., an intervertebral disc), various 3D voxelshapes and sizes can be used having various different dimensions (e.g.,a voxel having a smaller height may have a larger length and/or widthwhile remaining contained in the ROI than a voxel having a largerheight, which can have a smaller length and/or width in order to fitinto the ROI). The system can select a 3D voxel having a maximized orincreased volume. In some embodiments, one or more of the MRI images maynot contribute to the formation of the voxel, for example, if inclusionof an MRI image near the end of the ROI would require that the height ofthe voxel be reduced to a degree that would lower the total volume ofthe voxel, the MRI image near the end of the ROI can be disregarded forthe forming of the voxel. In some embodiments, the volume can bemaximized by defining a voxel for multiple combinations of MRI imagesand the voxel with the largest volume can be selected. For example, foran array of MRI images having 13 slices, voxels can be defined using 13slices, 12 slices, 11 slices, 10 slices, etc. The volumes for the voxelscan be calculated and compared and the voxel having the largest volumecan be selected (e.g., a voxel using 9 MRI images and omitting the 2 endslices from each side). In some embodiments, the voxel can be formed toprovide an increased or maximized dimension (e.g., height, width, orlength) of the voxel.

In some embodiments, the process can define the voxel contained withinan inward offset from the boundaries of the ROI. For example, the offsetcan provide a buffer that can prevent the voxel from covering non-ROItissue in the event of minor inaccuracies in voxelation and/or minorpatient movement during a procedure. In some cases, if the voxel weredefined to reach to the edge (or very close to the edge) of the ROI,patent movement during the examination or minor inaccuracies invoxelation can cause the voxel to cover a portion of non-ROI tissueduring a procedure, which can reduce the quality of the procedure asdiscussed above. The offset can be applied at various stages of theprocess. For example, in some embodiments, the final voxel can bereduced in size after voxelation. The population of pixels can bereduced in size after being populated, for example, by removing a layerof pixels (e.g., 1, 2, 5, 10 pixels, etc.) at the edge of the populationof pixels. The size of the shape that approximates the population ofpixels can be reduced in size, or the 2D voxel shapes can be reduced insize before they are used to form the 3D voxel. The process can beconfigured to increase or maximize the area of the voxel (similar to thedescription above) while containing the voxel within the inward offsetboundary of the ROI.

In some embodiments, multiple voxels can be formed for a single ROI. Forexample, a plurality of rectilinear voxels can be positioned inside ofan ellipsoidal shape to increase the amount of the ellipsoidal shapethat is included for a procedure. The plurality of voxels can havedifferent shapes and/or sizes. For example, a relatively large voxel canbe positioned at a central region of the ROI and one or more smallervoxels can be positioned around the larger voxel in the ROI. In someembodiments, the plurality of voxels can have the same size. Forexample, a standard voxel size and shape (e.g., cube or square) can beused and the process can be configured to fit the standard voxels intothe shape of the ROI.

Many other variations are possible. For example, in some embodiments,the y-axis (coronal axis) value can be set equal to the x-axis (sagittalaxis) value, thereby simplifying the voxel formation. In someembodiments, instead of modifying the 2D shape as needed at eachsagittal layer, the system can form the 2D shapes for each sagittallayer independent of the other layers, and the system can then definethe cross sectional shape of the 3D voxel to be the area shared by allthe 2D shapes when the 2D shapes are overlay on each other. In someembodiments, the 3D voxel can be a non-rectilinear volume shape. Forexample, the 3D voxel can be formed by connecting the multiple 2D shapes(e.g., by interpolating) to form a 3D voxel, which can have an irregularshape not defined by an equation or mathematical shape. In someembodiments, the system can skip the 2D voxel formulation phase 230. Forexample, the system can calculate a 3D model for the ROI based on thepopulations of pixels that are identified in the series of parallelsagittal slices. A 3D voxel can then be selected that fits into the 3Dmodel. For example, a 3D voxel can be selected that provides anincreased or substantially maximized volume for the given 3D model.

At block 245, the 3D voxel can be displayed, for example, so that a usercan inspect the 3D voxel. As shown in FIG. 10B, the 3D voxel can bedisplayed relative to a mid-sagittal MRI image, a mid-coronal MRI image,and/or an axial (or oblique axial) MRI image (not shown in FIG. 10B), sothat the 3D voxel can be compared to the ROI displayed on the MRIimages. In some embodiments, the system can allow the user to manipulatethe view of the 3D voxel for inspection at various angles. In someembodiments, the system can allow the user to manually adjust the sizeor shape of the 3D voxel. In some embodiments, block 245 can be omitted.In some embodiments, block 245 can be performed only if one or more ofthe prior operations caused the procedure to be flagged as beingpotentially unreliable.

At block 246, the system can display the voxel dimensions, coordinates,and/or angles for the user. In some embodiments, the voxel dimensions,coordinates, angles, and/or other voxel data can be transferred to anMRS system so that the 3D voxel can be used as a scan area during a MRSexam, as described above. The system can provide the 3D voxelinformation to the MRS system in machine (or world) coordinates. Theconversion from image coordinates to world coordinates can be performedat various stages of the process 200. For example, in some embodiments,the system can convert image coordinates for the pixels in thepopulation of pixels to world coordinates, and the formation of the 2Dmodel, 2D voxel, 3D model, and/or 3D voxel can be done in worldcoordinates. In some embodiments, the system can use image coordinatesfor formation of the 2D model, 2D voxel, 3D model, and/or 3D voxel, andthe final data transferred to the MRS system can be converted to worldcoordinates. It will be understood that the conversion from imagecoordinates to world coordinates can be performed at other stages of theprocess 200 than those specifically identified.

The methods and systems disclosed herein can perform one or more of theoperations of the 3D voxel formation phase 240 alone or with variouscombinations of the other components of the method shown in FIG. 5 ordescribed herein. Various components of the illustrated 3D voxelformation phase 240 can be combined, omitted, or supplemented withadditional components.

In some embodiments, the program can use images of different modes toimprove the accuracy of the voxelation process or to check the accuracy.Different modes of MRI images can be, for example, MRI images made usinga T1 process (first mode) and MRI images made using a T2 process (secondmode). The images can be made of the same tissue to allow for comparisonbetween the images of the different modes. The voxelation process can beperformed on the images of different modes, and the resulting voxelresults can be compared. Because images of different modes can havedifferences such as different contrast ratios applied to differenttissues, the resulting voxels can be different for each mode of imaging.The voxels produced using the different modes of images can be combined(e.g., averaged) to form a final voxel to be used in the MRS exam. Thus,if one mode of images does not sufficiently represent one aspect of thetissue being imaged, one or more of the other modes of images can betterrepresent that aspect of the tissue and improve the accuracy of thefinal voxel. The voxels produced by the different modes of images canalso be compared, and if the differences between the voxels is above athreshold level, the voxelation process can be flagged as potentiallyunreliable, can be restarted, can be aborted, or can be supplementedwith additional operations designed to minimize errors. In someembodiments, the information derived from the images of different modescan be compared or combined before voxel formation, for example afterdefining populations of pixels for the multiple images of differentmodes, the defined pixel populations can be combined (e.g., averaged) orcompared to confirm accuracy.

One further embodiment uses two different images of the same area oftissue via two different imaging modalities, and performs an imagefusion operation between them. In one embodiment, the fusion operationincludes a “differencing” between the images to produce a third“differencing” image. In another embodiment, the fusion operationcomprises performing a blending or merging between the images (e.g.alpha blend, for example). Such image fusion, according to these orother illustrative examples, is applied in certain embodiments foremphasizing contrast at certain particular tissue structures, orinterfaces between adjacent amorphous tissue structures—such as thatvary in different chemicals that are respectively emphasized by eachdifferent modality. One particular embodiment differences T1 andT2-weighted images, whereas another applies the fusion (e.g.differencing or blending) to emphasize contrast at the borders, e.g.bony boundaries, of musculoskeletal joints (e.g. adjacent vertebralbodies bordering intervertebral discs), whereas another applies this T1and T2-weighted image fusion to musculoskeletal joints, such as spinaljoints. According to these various embodiments, the results of thefusion themselves may provide various different benefits, either intheir own right for diagnostic image analysis, or for further downstreamoperations (such as for example consistent with other embodimentsdisclosed herein, e.g. for use in generating voxel prescription,generating related structural measurements, directing certain furtheroperations, or otherwise regarding segmented target regions of interest,or other purposes and combination uses).

FIGS. 11A-C illustrate one more detailed example applying this fusionapproach described immediately above for emphasizing image contrastalong pixel populations corresponding with vertebral body end-plateborders above and below intervertebral discs of spinal joints (in thiscase lumbar), and also elsewhere at other borders around such disc andvertebral body structures, as follows.

FIG. 11A shows a T1-weighted image of a lumbar region of a volunteerhuman test subject, taken via a commercial 3T MRI scanner. FIG. 11Bshows a T2-weighted image of the same lumbar region of the same subject,taken during the same imaging session on same scanner. FIG. 11C showsthe T1-T2 differencing result, again of the same lumbar region. As isclearly demonstrated in comparing these results, the differencing resultof FIG. 11C provides more significant contrast at the end-plate bordersabove and below each disc than is provided by either the T1 orT2-weighted images independently shown in FIGS. 11A-B, respectively.

This T1-T2 differencing shown in FIG. 11C was derived by processing theT1 and T2-weighted images of FIGS. 11A-B as follows. Differencing is abuilt-in capability on most scanner or image viewer systems, either forpost-processing after a study or such that a technician operator mayreadily do so at the time of an exam, e.g. to check for patient motion(which may be a valuable utility to determine if a rescan may berequired or a good idea—clearly best determined if possible while thepatient is still in the imaging modality). Commercially availablesoftware packages and related tools, such as for example “MATLAB”(Version 7.14 with Imaging Processing Toolbox Version 8.0), acommercially available programming language and environment fromMathWorks, Inc., provides the capability to directly perform imageprocessing operations, including fusion as described above. For example,the difference image of FIG. 11C was generated by the MATLAB codesegment:

T1T2Diff = imfuse(T1s6, −T2s6, ‘diff’); imshow(T1T2Diff, [ ]).where T1s6 and T2s6 are MATLAB intensity image matrices as formeddirectly from the corresponding DICOM files by the MATLAB function“dicomread”

FIG. 11D shows the result according to another illustrated embodiment,wherein the same T1 and T2-weighted images are “blended” or “merged”—inthis particular example according to an “alpha-blend” approach. Thisblended image is formed in a manner similar to deriving the differencedimage, as by the same MATLAB program but invoking instead the followingcode segment:

T1T2Blend = imfuse(−T1s6, −T2s6, ‘blend’); imshow(T1T2Blend, [ ]) .

It is also noted that the illustrated embodiments shown in FIGS. 11C and11D are “negative” images of the processed combined image approaches, asin the target contrast area of the end-plate borders were observed to beparticularly emphasized in that negative mode. The reference T1 andT2-weighted images shown in FIGS. 11A-B are shown with contrast andbrightness settings (range and center) as would typically be viewed.However, contrast can be varied to also vary the results. For example,FIGS. 11E and 11F show the same differencing result at lower and higherrelative contrast settings—with varied emphasis at the end-plate borders(which may impact how subsequent functions operate to segment the disc,or conversely vertebral bodies, between adjacent end-plates). Note alsothat the contrast is also clearly emphasized not just at the end-plateborders, but also along the lateral anterior and posterior walls of thespinal column, both along the vertebral bodies and disc regions (inparticular via bright white in FIG. 11F). This results in nearlycompletely defining the enclosed circumferential borders surroundingthese respective tissue structures of the spinal linkage system (e.g.serial vertebral bodies and discs), and are well permissive for successin further refinement operations such as edge detection and/or modelingfor use in other applied purposes (e.g. dimensional or volumetricmeasurements, voxel prescription, or other further aspects of thisdisclosure). The processed images are on same slice from a multi-sliceacquisition (in the particular case, slice 6).

In performing these imaging combining approaches, such as for exampledifferencing and/or to show evidence of motion between images taken attwo different time points, the field of views may be the same, or thetwo acquisitions may be performed with different resolutions. Forillustration, a T2-weighted image may be a 320×320 image while the T1maybe a 384×384 image for example. In this situation, they could beresampled to equalize them and then difference or otherwisecombination-process them. An acquisition protocol may also specify thatthe different images be collected under the same prescription tofacilitate differencing or other combination processing.

The differencing vs. blended approaches illustrated in these examplesmay vary or be roughly equivalent in terms of respective benefits for aspecific implementation. In this particular case, while roughlyequivalent, the blended approach may be slightly superior for endplateemphasis. Certain other aspects may impact results, such as for example“leveling,” which generally refers to compensating for the brightnessvariation that varies with signal strength as a function distance fromthe receive coil. On FIG. 11A, for example, the fat is much brighterposterior than anterior but not with such apparent difference on the T2of FIG. 11B—a difference which could potentially impact preferred imagesin a given case, such as one fusion approach vs. another.

An additional step in such image combining processing may also include aregistration operation, to ensure exact overlay anatomically between theimages (as they are taken at different times in the sequencing, andpatient motion may intervene). Commercial tools are available for suchregistering between two separate images, such as for example the MATLAB(referenced above) Image Processing Toolbox function: “imregister”.Registering may also be done for example by use of commerciallyavailable software packages or utilities, such as for example the sameMATLAB package referenced above for other operations, but for exampleusing a code segment such as the following:

[optimizer, metric] = imregconfig(‘multimodal’) T1Reg= imregister((T1s6,T2s6, ′rigid′, optimizer, metric);.

This transforms the first image, T1s6 to be in alignment with thesecond, T2s6 and returns the transformed (registered) image as T1Reg.The first line defines a set of parameters to the registration algorithmappropriate for registering images of different modalities, inparticular, having different brightness ranges.

In this particular example of FIGS. 11A-F, registering the separate T1and T2-weighted images was tried—although no need was apparent as theyare in essentially perfect registration in their original forms.

It is to be appreciated that these embodiments illustrated above byreference to FIGS. 11A-F are shown and described in context of an MRscanner operation, and in particular context of T1 and T2-weightedimages. However, the broad aspects contemplated may also be achieved byfurther embodiments involving different pulse sequence acquisitionapproaches (E.g. T1rho or T2*), a combination of more than 2 differentimages, or images taken on different imaging modalities than thesespecific examples.

FIG. 12 schematically illustrates an example embodiment of a system 1100that can be configured to perform the process 200, or some portionthereof, or some variation thereof. The system 1100 can include acomputer processor 1102 and computer readable medium 1104. The processor1102 can be a general purpose processor or a special purpose processor,and the computer readable medium 1104 can be, for example, a tangle,non-transitory computer readable medium such as a hard disk, anon-volatile memory module, a volatile memory module, an optical disc,etc. The computer readable medium 1104 can include computer instructions1106 (e.g., a software program) which can be configured to cause thesystem 1100 to perform the method 200, or some portion thereof, or somevariation thereof, as disclosed herein. In some embodiments, differentcode modules can be stored on separate computer storage devices ormedia, and can be executed by different processors or machines. In someembodiments, the computer readable medium can include one or more MRIimages 1108 to be used for voxelation or for otherwise analyzing a ROIrepresented in the MRI images.

In some embodiments, the system can include an MRI system 1110, whichcan be used to acquire the MRI images 1108. The system 1112 can alsoinclude an MRS system configured to perform an MRS exam, which can use avoxel provided by the system 1100 as a scan area. In some embodiments,the MRI system 1110 and the MRS system 1112 can be integrated into asingle system configured to generate MRI images and to perform MRSexaminations. In some embodiments, the MRI system 1110 and/or the MRSsystem 1112 can be omitted, and the system 1100 can perform voxelationwithout being connected to the MRI system 1110 or the MRS system 1112.The components of the system 1100 can be in communication with eachother and can be located in close proximity to each other. For example,the entire system 1100 can be integrated into a single device (e.g.,with a computer system integrated into a joint MRI/MRS system). Thecomponents of the system 1100 can be located in a single room or withinthe same building (e.g., a hospital). In some embodiments, somecomponents of the system 1100 can be located remotely from othercomponents. For example, the MRI system 1110 and/or the MRS system 1112can be located remotely from the processor 1102 and computer memory1104, and a communication connection can be established using theinternet or a network. In some embodiments, the prescription of one ormore voxels, or other analysis of the ROI, can be performed by adifferent system or by a different party than the system or party thatacquires the images 1108 and/or performs the MRS exam. In someembodiments, the prescription of one or more voxels, or other analysisof the ROI, can be performed at a later time and/or at a different placethan the acquisition of the images 1108 and/or than the MRS exam.

In some embodiments, the MRI images can be acquired (e.g., by MRI system1110) using a first acquisition mode (e.g., T2 MRI imaging) and an MRSprocedure (e.g., performed by MRS system 1112) can be performed using asecond acquisition mode (e.g., T1 rho MRI imaging). Thus, in someembodiments, the ROI locating and voxelation can be performed based ondata obtained from the first acquisition mode (e.g., T2) and the voxelcan be used for a procedure or exam that uses the second acquisitionmode (e.g., T1 rho).

In some embodiments, information provided by the ROI locating orvoxelation process can be used for treatment of a patient. For example,a voxel can identify a target area in the patient's body for treatmentsuch as radiation therapy, high-intensity focused ultrasound therapy, orvarious other procedures.

Although various embodiments are described herein in connection withnuclear magnetic resonance (MR) processes such as MRI and MRS, otherimaging and analysis processes can be used. For example, a CT system, anX-ray system, a PET imaging system, or other imaging systems can be usedfor identifying or analyzing the ROI. Thus, although many embodimentsdiscuss the use of MRI images, it will be understood that CT images, PETimages, X-ray images, or images of a different modality can be used incombination with or instead of the MRI images discussed herein. Also,although some embodiments discuss the use of the voxel or ROIinformation in connection with an MRS exam procedure, it will beunderstood that CT, PET, X-ray, and other procedures can be performedbased on the voxel or ROI information.

FIG. 13 is a flow diagram showing various example methods. In someembodiments, a method can start at block 1202 by processing an MRI imageas described herein, and at block 1204, the system can identify apopulation of pixels relating to the ROI in the MRI image. The processcan proceed to block 1206 where the system can form a 2D model of theROI shown in the MRI image using the population of pixels. The 2D modelcan be used to form a 2D voxel shape at block 1208. In some embodiments,the system can form a 2D voxel shape directly from the population ofpixels, without forming a 2D model of the ROI (as shown by the arrowbetween blocks 1204 and 1208). The process can proceed to block 1210after forming a 2D model or a 2D voxel shape, or the process can proceedto block 1210 after identifying a population of pixels for the MRI image(e.g., without forming a 2D model or voxel shape). At block 1210, ifadditional MRI images are to be analyzed, the process can move to block1212 to access the next MRI image, and then proceed back to block 1202to repeat. If no additional MRI images are to be analyzed, the processcan proceed to block 1214 to form a 3D model of the ROI (e.g., usingdata from the populations of pixels from the MRI images or bycalculating a composite of the 2D models made at block 1206 for the MRIimages). The process can advance to block 1216 and use the 3D model toform a 3D voxel that fits into the 3D model of the ROI. The process canalso advance from block 1210 to block 1216 to create a 3D voxel withouta 3D model. For example, the process can form a 3D voxel by expandingone of the 2D voxel shapes (formed at block 1208) across the width ofthe MRI image slices, and the process can adjust the shape of the 2Dvoxel shape as needed to remain inside the ROI for each MRI image slice,as described above.

The lines leading from blocks 1204, 1206, 1208, 1214, and 1216 to theblock 1218 illustrate that at various stages of the process, data can beconverted from image coordinates to world coordinates (e.g., for use inan MRS exam or for locating a ROI). For example, in some embodiments,the image coordinates corresponding to the population of pixels can beconverted to world coordinates that represent the location of a ROI.Thus, in some embodiments, the method can be performed for a singleimage and can proceed from block 1202, to 1204, and then to 1218. Insome embodiments, one or more 2D models (for a single MRI image ormultiple MRI images), or a 3D model can be converted to worldcoordinates for identifying the location, size, and/or orientation ofthe ROI. Thus, in some embodiments, the method does not create a voxelto define a scan area for an MRS exam, and the process can merelyprovide information about the ROI (e.g., size, location, orientation) toa user or to a system. In some embodiments, the world coordinates can beused for an MRS exam or other procedure or can be reported to a user orsystem for additional analysis. In some embodiments, the worldcoordinates can be used for additional portions of the processesdisclosed in FIG. 12. For example, a 3D model formed at block 1214 canbe converted to world coordinates at block 1218 and the worldcoordinates can be used to form a 3D voxel at block 1216. Also, the 2Dvoxel shapes formed at block 1208 can be converted to world coordinatesand the world coordinates can then be used for forming the 3D voxel atblock 1216. Many other variations are possible. Alternative flow pathsthrough the flow cart of FIG. 13 are possible other than thosespecifically discussed, and are contemplated as part of this disclosure.The methods illustrated by FIG. 13 can be implemented by a system suchas system 1100 of FIG. 12.

The following United States Patent Application Publications are hereinincorporated in their entirety by reference thereto: US 2005/0240104, US2010/0086185, US 2010/0268225.

The following additional references are also herein incorporated in itsentirety by reference thereto:

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Hao S et al., “[Spine disc MR image analysis using improved independentcomponent analysis based active appearance model and Markov randomfield].” Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2010 February;27(1):6-9, 15 [Article in Chinese]

-   Horsfield M A et al., “Rapid semi-automatic segmentation of the    spinal cord from magnetic resonance images: application in multiple    sclerosis.” Neuroimage. 2010 Apr. 1; 50(2):446-55.-   Bechara B P et al., “Application of a semiautomated contour    segmentation tool to identify the intervertebral nucleus pulposus in    MR images.” AJNR Am J Neuroradiol. 2010 October; 31(9):1640-4.-   Ben Ayed I et al., “Graph cuts with invariant object-interaction    priors: application to intervertebral disc segmentation.” Inf    Process Med Imaging. 2011; 22:221-32.-   Dalca A et al., “Segmentation of nerve bundles and ganglia in spine    MRI using particle filters.” Med Image Comput Comput Assist Interv.    2011; 14(Pt 3):537-45.-   Michopoulou S et. al., “Texture-based quantification of lumbar    intervertebral disc degeneration from conventional T2-weighted MRI,”    Acta Radiologica 2011; 52: 91-98.-   Neubert A, “Automated 3D Segmentation of Vertebral Bodies and    Intervertebral Discs from MRI,” 2011 International Conference on    Digital Image Computing: Techniques and Applications.-   Strickland C G et al., “Development of subject-specific geometric    spine model through use of automated active contour segmentation and    kinematic constraint-limited registration.” J Digit Imaging. 2011    October; 24(5):926-42.-   Giulietti G et al., “Semiautomated segmentation of the human spine    based on echoplanar images,” Magn Reson Imaging. 2011 December;    29(10):1429-36.-   Stern D et al., “Parametric modelling and segmentation of vertebral    bodies in 3D CT and MR spine images.” Phys Med Biol. 2011 Dec. 7;    56(23):7505-22.-   Neubert A et. al., “Automated detection, 3D segmentation and    analysis of high resolution spine MR images using statistical shape    models.” Phys Med Biol. 2012 Dec. 21; 57(24):8357-76. Egger J et    al., “Square-cut: a segmentation algorithm on the basis of a    rectangle shape.” PLoS One. 2012; 7(2).-   Vrtovec T et al., “Automated curved planar reformation of 3D spine    images.” Phys Med Biol. 2005 Oct. 7; 50(19):4527-40.

As would be apparent to one of ordinary skill, combinations andsub-combinations between the disclosed aspects, modes, embodiments,features, and variations of references that are incorporated hereunderby reference thereto, and the various aspects, modes, embodiments,features, and variations shown and/or described in the presentdisclosure, are further contemplated as part of and falling with theintended scope of this disclosure.

It is to be appreciated that the foregoing description provides manydetails with respect to the embodiments shown and/or described. Thespecific details disclosed are intended to provide one of ordinary skillsufficient detailed examples to gain a full and complete understandingof the broader aspects of the present disclosure. While consideredhighly beneficial and illustrative of useful specific applications ofthe broader aspects contemplated hereunder, such details however are notintended to be necessarily limiting to such broader aspects, as would beapparent to one of ordinary skill. The present disclosure describesvarious features, no single one of which is solely responsible for thebenefits described herein. It will be understood that various featuresdescribed herein may be combined, modified, or omitted, as would beapparent to on of ordinary skill.

The present description provides for an automated voxelation system andmethod useful for providing voxels (e.g., for single voxel MRS exams),with particular application described for lumbar intervertebral discnuclei. However, the broad aspects may be applied to other specificapplications without departing from the broad intended scope hereof,such as larger disc volumes to include annulus (often becoming lessdistinguished from nuclei in degenerative discs), other disc levelsalong the spine, or other structures of the body where single voxel MRSmight be performed. Also, the methods and systems described herein canbe used in connection with multivoxel MRS exams.

Although many of the methods and systems described herein are describedas using sagittal images to form a voxel, or to model a ROI, orotherwise provide information relating to a ROI, in some embodiments,coronal or axial (or oblique axial) images can also be used. For examplea series of coronal plane slices can be used to create a 3D voxel, forexample, by creating 2D voxel shapes for the coronal images, modifyingthe 2D voxel shapes as needed to remain in the ROI, and defining a voxellength (in the y-axis) from the distance covered by the series ofcoronal images. Other methods disclosed herein can also be modified touse coronal or axial (or oblique axial) images where sagittal images aredescribed.

Furthermore, it is also contemplated that the various specificapproaches taken among the various methods herein described for thespecific application of automated voxelation also have other beneficialuses than only in such overall system and method. For example, thecurrent disclosure describes a system and method for automaticallyestimating the location, shape, and volume of intervertebral disc nucleibased on MRI images from an otherwise standard MRI exam. While this canbe beneficial for further use in the additional automated voxelationapproaches further developed in these detailed embodiments, thisapproach and result alone is considered an independent, beneficialaspect of the present disclosure, with many beneficial usescontemplated. For example, such result may be applied as useful forother MR-based exams and pulse sequences, such as for example estimatingT1 or T2 signal intensities, T1-rho data, etc. for the defined ROI.

Similarly, the voxelated results of the disclosure may be used for otherapplications than merely MRS, such as immediately described above. Byassigning a voxel to MRS data and also to other data acquired for thesame region but using a different MR modality, certain benefits mayarise from such combination. For example, MRS and T1-rho values can bothbe taken for the same ROI or voxel, such as for example in a complexmulti-pulse sequence exam of disc chemistry, which may bealgorithmically diagnostically useful, such as for example incalibrating an MRS curve based on NAA/proteoglycan peak regioncalibration against T1-rho-based calculations for the same. In yet afurther example of other contemplated applications of various disclosedaspects, the ability to automatically and accurately calculate discangle, disc height, and other tissue structure-related aspects areconsidered of broad value and application, beyond only the specificfurther embodiments to which such accomplishments are put in theexamples provided herein.

In some embodiments, the location of the disc (e.g., the center of thedisc nucleus) and the orientation of the disc (e.g., the angle of tilt)can be automatically identified as described herein and can be used tofacilitate the prescription of clinical MRI imaging sequences in thesagittal, coronal, and axial or oblique axial directions. The locationand orientation of the disc can be used to prescribe the location andorientation for MRI image slices to be used in clinical analysis. Thiscan be particularly advantageous in the axial or oblique axialorientation where the angle of disc tilt can vary significantly betweendiscs and between patients.

Still further, the approaches taken in the current embodiments may bemodified by one of ordinary skill without departing from the scope ofthe broadly intended aspects of this disclosure. For example, one ormore edge detection algorithms (e.g. contrast based, filter assisted,etc.) may be used to estimate the location and dimensions of thevertebral body end-plates bordering above and below discs, to facilitateexclusion of the end-plates from a disc voxelation and MRS exam. Theseperimeter structures may themselves be estimated to prescribe aperimeter around a disc, from the “outside-in” approach, and in which avoxel prescription is then optimized. Thus, in some embodiments, themethods and systems described herein can identify a population of pixelsassociated with a structure adjacent the ROI or otherwise useful indetermining the location of the ROI. A model of the ROI and/or a voxeldefining a scan area can then be created in a manner similar to thedescriptions above but wherein the populations of pixels are used todefine the ROI from the “outside-in” approach instead of defining theROI directly. In some embodiments, the anterior and posterior bordersare less defined than the superior and inferior end-plates (which aretypically well defined via T1 and/or T2 MRI contrast). The system canautomatically connect the anterior and posterior ends of the curvilinearestimated lines for the superior and inferior end-plates to define theanterior and posterior borders to appropriately encapsulate the disc inat least many cases, if not nearly all cases with only fringeexceptions. This is one example of another viable approach differentthan certain specific approaches shown and described herein by referenceto the detailed illustrative embodiments and figures, yet are consideredwithin the scope contemplated herein and representative of the broadintended aspects of this disclosure.

While the present description is primarily directed toward automatedvoxelation systems and methods, it is also to be appreciated that suchdisclosure may be applied in whole or in part as to the embodimentsdescribed, and thus provide for more fully or only partial automation ofthe voxelation, or related image reconstruction, or region of interestestimation processes. Provisions for certain aspects to be automated,and certain aspects to be manual, may be made. For example, auser/operator technician can click on estimated centers of disc levelsintended to be voxelated in order to indicate their general locationrelative to the field of view in the image. Such manual operations caneither replace some of the automated estimation operations providedherein (e.g., replacing the use of empirically derived default ellipsoidsearch areas), or may rather enhance the likelihood of accurate results(e.g., to better refine the locations at or by which to apply the otherestimation algorithms). Moreover, manual overrides may be provided, atoperator option. For example, after automated voxelation, the users maybe permitted to re-prescribe or modify the automated result based ontheir own observations, and/or knowledge of patient movement. In somecases the result of the automated voxelation can be automaticallyflagged for manual adjustment or inspection, as described herein. Forexample, if a potential source of error is observed by the automatedsystem, the system may prompt the user to inspect or modify theautomatically generated voxel. In these regards, the interim operationstaken by the automation system may be made invisible to the operator, orall or various operations may be made available to user for review,which may aid in interpreting results. In some cases the operations canbe made available to a reviewer of the results after completion of thevoxelation or MRS exam report.

In some embodiments, the system can perform a post-acquisition scan forvalidation that the subject did not move during the MRS dataacquisition. The system can automatically locate the targeted disc afterthe MRS data acquisition and compare that post-acquisition position tothe pre-acquisition position to determine if the voxel prescriptionremains valid after the data acquisition. In some embodiments, thepost-acquisition scan can be faster than the initial scan. For example,in some cases only a limited area is scanned that is near the originalposition of the disc. In some embodiments, only a post-acquisitionmid-sagittal scan is compared to the pre-acquisition mid-sagittal scanto assess patient movement. In some cases, the post-acquisition disclocation can be identified similarly as described above in connectionwith the pre-acquisition scan, and the coordinates of the pre andpost-acquisition scans can be compared and the acquisition can be deemedinvalid if the difference is greater than a threshold value.

The present disclosure describes various features, no single one ofwhich is solely responsible for the benefits described herein. It willbe understood that various features described herein may be combined,modified, or omitted, as would be apparent to one of ordinary skill.Other combinations and sub-combinations than those specificallydescribed herein will be apparent to one of ordinary skill, and areintended to form a part of this disclosure. Various methods aredescribed herein in connection with various flowchart steps and/orphases. It will be understood that in many cases, certain steps and/orphases may be combined together such that multiple steps and/or phasesshown in the flowcharts can be performed as a single step and/or phase.Also, certain steps and/or phases can be broken into additionalsub-components to be performed separately. In some instances, the orderof the steps and/or phases can be rearranged and certain steps and/orphases may be omitted entirely. Also, the methods described herein areto be understood to be open-ended, such that additional steps and/orphases to those shown and described herein can also be performed.

Some aspects of the systems and methods described herein canadvantageously be implemented using, for example, computer software,hardware, firmware, or any combination of computer software, hardware,and firmware. Computer software can comprise computer executable codestored in a computer readable medium (e.g., non-transitory computerreadable medium) that, when executed, performs the functions describedherein. In some embodiments, computer-executable code is executed by oneor more general purpose computer processors. A skilled artisan willappreciate, in light of this disclosure, that any feature or functionthat can be implemented using software to be executed on a generalpurpose computer can also be implemented using a different combinationof hardware, software, or firmware. For example, such a module can beimplemented completely in hardware using a combination of integratedcircuits. Alternatively or additionally, such a feature or function canbe implemented completely or partially using specialized computersdesigned to perform the particular functions described herein ratherthan by general purpose computers.

Multiple distributed computing devices can be substituted for any onecomputing device described herein. In such distributed embodiments, thefunctions of the one computing device are distributed (e.g., over anetwork) such that some functions are performed on each of thedistributed computing devices.

Some embodiments may be described with reference to equations,algorithms, and/or flowchart illustrations. These methods may beimplemented using computer program instructions executable on one ormore computers. These methods may also be implemented as computerprogram products either separately, or as a component of an apparatus orsystem. In this regard, each equation, algorithm, block, or step of aflowchart, and combinations thereof, may be implemented by hardware,firmware, and/or software including one or more computer programinstructions embodied in computer-readable program code logic. As willbe appreciated, any such computer program instructions may be loadedonto one or more computers, including without limitation a generalpurpose computer or special purpose computer, or other programmableprocessing apparatus to produce a machine, such that the computerprogram instructions which execute on the computer(s) or otherprogrammable processing device(s) implement the functions specified inthe equations, algorithms, and/or flowcharts. It will also be understoodthat each equation, algorithm, and/or block in flowchart illustrations,and combinations thereof, may be implemented by special purposehardware-based computer systems which perform the specified functions orsteps, or combinations of special purpose hardware and computer-readableprogram code logic means.

Furthermore, computer program instructions, such as embodied incomputer-readable program code logic, may also be stored in a computerreadable memory (e.g., a non-transitory computer readable medium) thatcan direct one or more computers or other programmable processingdevices to function in a particular manner, such that the instructionsstored in the computer-readable memory implement the function(s)specified in the block(s) of the flowchart(s). The computer programinstructions may also be loaded onto one or more computers or otherprogrammable computing devices to cause a series of operational steps tobe performed on the one or more computers or other programmablecomputing devices to produce a computer-implemented process such thatthe instructions which execute on the computer or other programmableprocessing apparatus provide steps for implementing the functionsspecified in the equation(s), algorithm(s), and/or block(s) of theflowchart(s).

All of the methods and tasks described herein may be performed and fullyautomated by a computer system. The computer system may, in some cases,include multiple distinct computers or computing devices (e.g., physicalservers, workstations, storage arrays, etc.) that communicate andinteroperate over a network to perform the described functions. Eachsuch computing device typically includes a processor (or multipleprocessors) that executes program instructions or modules stored in amemory or other non-transitory computer-readable storage medium ordevice. The various functions disclosed herein may be embodied in suchprogram instructions, although some or all of the disclosed functionsmay alternatively be implemented in application-specific circuitry(e.g., ASICs or FPGAs) of the computer system. Where the computer systemincludes multiple computing devices, these devices may, but need not, beco-located. The results of the disclosed methods and tasks may bepersistently stored by transforming physical storage devices, such assolid state memory chips and/or magnetic disks, into a different state.

1.-220. (canceled)
 221. A method for obtaining information relating to aregion of interest, the method comprising: accessing a plurality ofelectronic magnetic resonance imaging (MRI) images of an area thatincludes a region of interest; for each of the plurality of electronicMRI images: automatically processing the electronic MRI image, using oneor more computer processors, to emphasize pixels associated with aborder between the region of interest and structure adjacent to theregion of interest; automatically identifying, using the one or morecomputer processors, a population of pixels in the electronic MRI imageassociated with the border; and automatically fitting, using the one ormore computer processors, a two dimensional shape inside a cross-sectionof the region of interest and within the border shown by the electronicMRI image based on the population of pixels; automatically generating,using the one or more computer processors, world coordinates that definea three dimensional selected volume comprising a voxel that fits insideof the region of interest and excludes the border based on one or moreof the two dimensional shapes; and scanning the voxel with a magneticresonance spectroscopy (MRS) system in communication with the one ormore computer processors to provide an MRS spectrum of chemicalconstituents within the voxel.
 222. The method of claim 221, wherein theregion of interest comprises an intervertebral disc.
 223. The method ofclaim 222, wherein the structure adjacent to the region of interestcomprises a superior vertebral body and an inferior vertebral body. 224.The method of claim 221, wherein the two dimensional shape comprises arectilinear shape.
 225. The method of claim 221, wherein the voxelcomprises a rectilinear volume.
 226. The method of claim 221,comprising: determining, using the one or more computer processors, atwo dimensional model that approximates the region of interest based onthe population of pixels; and orienting, using the one or more computerprocessors, the two dimensional shape based on the two dimensionalmodel.
 227. The method of claim 226, wherein the two dimensional modelcomprises an ellipsoid shape having a semi-major axis and a semi-minoraxis, and wherein the two dimensional shape comprises a rectangle havinga length and a width that are oriented based on the orientation of thesemi-major axis and/or the semi-minor axis of the ellipsoid shape. 228.The method of claim 226, wherein calculating the two dimensional modelcomprises applying an expectation maximization algorithm for estimatingparameters of one or more Gaussian distributions for the population ofpixels.
 229. The method of claim 221, wherein the plurality ofelectronic MRI images are of slices substantially parallel to, andspaced apart from, each other.
 230. The method of claim 221, comprisingdefining one or more additional voxels covering at least portions of oneor more additional regions of interest, and scanning the one or moreadditional voxels one at a time with the MRS system.
 231. The method ofclaim 221, comprising defining one or more additional voxels covering atleast portions of one or more additional regions of interest, andscanning the additional voxels simultaneously with the MRS system. 232.The method of claim 221, wherein the voxel has a cross sectional shapecorresponding to the overlapping area of a plurality of the twodimensional shapes.
 233. The method of claim 221, wherein processing theelectronic image comprises smoothing the electronic image.
 234. Themethod of claim 221, wherein processing the electronic image comprisesmodifying a brightness value for a pixel based on the brightness ofneighboring pixels.
 235. The method of claim 234, wherein theneighboring pixels comprise one or more pixels from one or moreneighboring electronic MRI images.
 236. The method of claim 221, whereinprocessing the electronic image comprises performing one or more of: atleast one top-hat filtering operation, at least one morphological imageprocessing operation, and an order statistic filtering operation. 237.The method of claim 221, wherein the electronic MRI images are of afirst imaging mode, and wherein the method further comprises: accessingan additional electronic image of a second imaging mode different thanthe first imaging mode, the additional electronic image of the secondimaging mode being of substantially the same area as a correspondingelectronic MRI image of the first imaging mode; and automaticallyidentifying, using the one or more computer processors, an additionalpopulation of pixels in the additional electronic image associated withthe border between the region of interest and the structure adjacent tothe region of interest; wherein the world coordinates are based at leastin part on the additional population of pixels.
 238. The method of claim221, comprising: generating first world coordinates that define a firstvolume that fits inside the region of interest; generating second worldcoordinates that define a second volume that fits inside the region ofinterest based on a different number of electronic MRI images than usedfor generating the first volume; calculating a volume size of the firstvolume; calculating a volume size of the second volume; and selecting asthe voxel the one of the first volume and the second volume that has thelarger volume size.
 239. The method of claim 221, wherein one or more ofthe plurality of electronic MRI images are disregarded when generatingthe world coordinates that define the voxel.
 240. The method of claim221, wherein the voxel is fit within an inward offset from the border.241. One or more non-transitory computer readable media comprisingcomputer instructions configured to cause one or more computerprocessors to perform actions comprising: accessing a plurality ofelectronic magnetic resonance imaging (MRI) images of an area thatincludes a region of interest; for each of the plurality of electronicMRI images: processing the electronic MRI image to emphasize pixelsassociated with a border between the region of interest and neighboringstructure; identifying a population of pixels in the electronic MRIimage associated with the border; and fitting a two dimensional shapeinside a cross-section of the region of interest and within the bordershown by the electronic MRI image based on the population of pixels; andgenerating world coordinates that define a voxel that fits inside of theregion of interest and excludes the border based on one or more of thetwo dimensional shapes; wherein the one or more non-transitorycomputer-readable media are configured to be used with a magneticresonance spectroscopy (MRS) system in communication with the one ormore computer processors, and the MRS system is configured to provide anMRS spectrum of chemical constituents within the voxel.
 242. The one ormore non-transitory computer readable media of claim 241, wherein theregion of interest comprises an intervertebral disc.
 243. The one ormore non-transitory computer readable media of claim 242, wherein theneighboring structure comprises a superior vertebral body and aninferior vertebral body.
 244. The one or more non-transitory computerreadable media of claim 241, wherein the two dimensional shape comprisesa rectilinear shape.
 245. The one or more non-transitory computerreadable media of claim 241, wherein the voxel comprises a rectilinearvolume.
 246. The one or more non-transitory computer readable media ofclaim 241, wherein the actions comprise: determining a two dimensionalmodel that approximates the region of interest based on the populationof pixels; and orienting the two dimensional shape based on the twodimensional model.
 247. The one or more non-transitory computer readablemedia of claim 246, wherein the two dimensional model comprises anellipsoid shape having a semi-major axis and a semi-minor axis, andwherein the two dimensional shape comprises a rectangle having a lengthand a width that are oriented based on the orientation of the semi-majoraxis and/or the semi-minor axis of the ellipsoid shape.
 248. The one ormore non-transitory computer readable media of claim 246, whereincalculating the two dimensional model comprises applying an expectationmaximization algorithm for estimating parameters of one or more Gaussiandistributions for the population of pixels.
 249. The one or morenon-transitory computer readable media of claim 241, wherein theplurality of electronic MRI images are of slices substantially parallelto, and spaced apart from, each other.
 250. The one or morenon-transitory computer readable media of claim 241, wherein the voxelhas a cross sectional shape corresponding to the overlapping area of aplurality of the two dimensional shapes.
 251. The one or morenon-transitory computer readable media of claim 241, wherein processingthe electronic image comprises smoothing the electronic image.
 252. Theone or more non-transitory computer readable media of claim 241, whereinprocessing the electronic image comprises modifying a brightness valuefor a pixel based on the brightness of neighboring pixels.
 253. The oneor more non-transitory computer readable media of claim 252, wherein theneighboring pixels comprise one or more pixels from one or moreneighboring electronic MRI images.
 254. The one or more non-transitorycomputer readable media of claim 241, wherein processing the electronicimage comprises performing one or more of: at least one top-hatfiltering operation, at least one morphological image processingoperation, and an order statistic filtering operation.
 255. The one ormore non-transitory computer readable media of claim 241, wherein theelectronic MRI images are of a first imaging mode, and wherein theactions comprise: accessing an additional electronic image of a secondimaging mode different than the first imaging mode, the additionalelectronic image of the second imaging mode being of substantially thesame area as a corresponding electronic MRI image of the first imagingmode; and identifying an additional population of pixels in theadditional electronic image associated with the border between theregion of interest and the neighboring structure; wherein the worldcoordinates are based at least in part on the additional population ofpixels.
 256. The one or more non-transitory computer readable media ofclaim 241, wherein the actions comprise: generating first worldcoordinates that define a first volume that fits inside the region ofinterest; generating second world coordinates that define a secondvolume that fits inside the region of interest based on a differentnumber of electronic MRI images than used for generating the firstvolume; calculating a volume size of the first volume; calculating avolume size of the second volume; and selecting as the voxel the one ofthe first volume and the second volume that has the larger volume size.257. The one or more non-transitory computer readable media of claim241, wherein one or more of the plurality of electronic MRI images aredisregarded when generating the world coordinates that define the voxel.258. The one or more non-transitory computer readable media of claim241, wherein the voxel is fit within an inward offset from the border.259. A system comprising: a magnetic resonance spectroscopy (MRS)system; one or more computer processors in communication with the MRSsystem; and the one or more non-transitory computer readable media ofclaim
 241. 260. The system of claim 259, wherein the MRS system has afirst operational mode for producing the plurality of electronic MRIimages, and wherein the MRS system has a second operational mode forproducing the MRS spectrum.